IE-en/NER/CrossNER_AI/test.json

{"text": "Typical generative model approaches include naive Bayes classifier s , Gaussian mixture model s , variational autoencoders and others .", "entity": [{"entity": "naive Bayes classifier", "entity_type": "algorithm", "pos": [44, 66]}, {"entity": "Gaussian mixture model", "entity_type": "algorithm", "pos": [71, 93]}, {"entity": "variational autoencoders", "entity_type": "algorithm", "pos": [98, 122]}], "task": "NER"}
{"text": "Finally , every other year , ELRA organizes a major conference LREC , the International Language Resources and Evaluation Conference .", "entity": [{"entity": "ELRA", "entity_type": "conference", "pos": [29, 33]}, {"entity": "LREC", "entity_type": "conference", "pos": [63, 67]}, {"entity": "International Language Resources and Evaluation Conference", "entity_type": "conference", "pos": [74, 132]}], "task": "NER"}
{"text": "The task is usually to derive the maximum likelihood estimate of the parameters of the HMM given the of output sequences .", "entity": [{"entity": "maximum likelihood estimate", "entity_type": "algorithm", "pos": [34, 61]}, {"entity": "HMM", "entity_type": "algorithm", "pos": [87, 90]}], "task": "NER"}
{"text": "Unlike neural network s and Support vector machine , the AdaBoost training process selects only those features known to improve the predictive power of the model , reducing dimensionality and potentially improving execution time as irrelevant features need not be computed .", "entity": [{"entity": "neural network", "entity_type": "algorithm", "pos": [7, 21]}, {"entity": "Support vector machine", "entity_type": "algorithm", "pos": [28, 50]}, {"entity": "AdaBoost", "entity_type": "algorithm", "pos": [57, 65]}], "task": "NER"}
{"text": "Troponymy is one of the possible relations between verb s in the semantic network of the WordNet database .", "entity": [{"entity": "Troponymy", "entity_type": "else", "pos": [0, 9]}, {"entity": "semantic network", "entity_type": "else", "pos": [65, 81]}, {"entity": "WordNet database", "entity_type": "product", "pos": [89, 105]}], "task": "NER"}
{"text": "A frame language is a technology used for knowledge representation in artificial intelligence .", "entity": [{"entity": "knowledge representation", "entity_type": "task", "pos": [42, 66]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [70, 93]}], "task": "NER"}
{"text": "NIST also differs from Bilingual evaluation understudy in its calculation of the brevity penalty insofar as small variations in translation length do not impact the overall score as much .", "entity": [{"entity": "NIST", "entity_type": "metrics", "pos": [0, 4]}, {"entity": "Bilingual evaluation understudy", "entity_type": "metrics", "pos": [23, 54]}, {"entity": "brevity penalty", "entity_type": "else", "pos": [81, 96]}], "task": "NER"}
{"text": "The model is initially fit on a training dataset , The model ( e.g. a neural net or a naive Bayes classifier ) is trained on the training dataset using a supervised learning method , for example using optimization methods such as gradient descent or stochastic gradient descent .", "entity": [{"entity": "neural net", "entity_type": "algorithm", "pos": [70, 80]}, {"entity": "naive Bayes classifier", "entity_type": "algorithm", "pos": [86, 108]}, {"entity": "supervised learning", "entity_type": "field", "pos": [154, 173]}, {"entity": "gradient descent", "entity_type": "algorithm", "pos": [230, 246]}, {"entity": "stochastic gradient descent", "entity_type": "algorithm", "pos": [250, 277]}], "task": "NER"}
{"text": "FrameNet has been used in applications like question answering , paraphrasing , recognizing textual entailment , and information extraction , either directly or by means of Semantic Role Labeling tools .", "entity": [{"entity": "FrameNet", "entity_type": "product", "pos": [0, 8]}, {"entity": "question answering", "entity_type": "task", "pos": [44, 62]}, {"entity": "paraphrasing", "entity_type": "task", "pos": [65, 77]}, {"entity": "recognizing textual entailment", "entity_type": "task", "pos": [80, 110]}, {"entity": "information extraction", "entity_type": "task", "pos": [117, 139]}, {"entity": "Semantic Role Labeling", "entity_type": "task", "pos": [173, 195]}], "task": "NER"}
{"text": "This would include programs such as data analysis and extraction tools , spreadsheets ( e.g. Excel ) , databases ( e.g. Access ) , statistical analysis ( e.g. SAS ) , generalized audit software ( e.g. ACL , Arbutus , EAS ) , business intelligence ( e.g. Crystal Reports and Business Objects ) , etc .", "entity": [{"entity": "data analysis", "entity_type": "field", "pos": [36, 49]}, {"entity": "spreadsheets", "entity_type": "else", "pos": [73, 85]}, {"entity": "Excel", "entity_type": "product", "pos": [93, 98]}, {"entity": "databases", "entity_type": "else", "pos": [103, 112]}, {"entity": "Access", "entity_type": "product", "pos": [120, 126]}, {"entity": "statistical analysis", "entity_type": "field", "pos": [131, 151]}, {"entity": "SAS", "entity_type": "product", "pos": [159, 162]}, {"entity": "generalized audit software", "entity_type": "else", "pos": [167, 193]}, {"entity": "ACL", "entity_type": "product", "pos": [201, 204]}, {"entity": "Arbutus", "entity_type": "product", "pos": [207, 214]}, {"entity": "EAS", "entity_type": "product", "pos": [217, 220]}, {"entity": "business intelligence", "entity_type": "else", "pos": [225, 246]}, {"entity": "Crystal Reports", "entity_type": "product", "pos": [254, 269]}, {"entity": "Business Objects", "entity_type": "product", "pos": [274, 290]}], "task": "NER"}
{"text": "Rethink Robotics - founded by Rodney Brooks , previously with iRobot - introduced Baxter in September 2012 ; as an industrial robot designed to safely interact with neighboring human workers , and be programmable for performing simple tasks .", "entity": [{"entity": "Rethink Robotics", "entity_type": "organization", "pos": [0, 16]}, {"entity": "Rodney Brooks", "entity_type": "researcher", "pos": [30, 43]}, {"entity": "iRobot", "entity_type": "organization", "pos": [62, 68]}, {"entity": "Baxter", "entity_type": "product", "pos": [82, 88]}, {"entity": "industrial robot", "entity_type": "product", "pos": [115, 131]}], "task": "NER"}
{"text": "Typical text mining tasks include text categorization , text clustering , concept / entity extraction , production of granular taxonomies , sentiment analysis , document summarization , and entity relation modeling ( i.e. , learning relations between named entity recognition ) .", "entity": [{"entity": "text mining", "entity_type": "field", "pos": [8, 19]}, {"entity": "text categorization", "entity_type": "task", "pos": [34, 53]}, {"entity": "text clustering", "entity_type": "task", "pos": [56, 71]}, {"entity": "concept / entity extraction", "entity_type": "task", "pos": [74, 101]}, {"entity": "production of granular taxonomies", "entity_type": "task", "pos": [104, 137]}, {"entity": "sentiment analysis", "entity_type": "task", "pos": [140, 158]}, {"entity": "document summarization", "entity_type": "task", "pos": [161, 183]}, {"entity": "entity relation modeling", "entity_type": "task", "pos": [190, 214]}, {"entity": "named entity recognition", "entity_type": "task", "pos": [251, 275]}], "task": "NER"}
{"text": "Nonetheless , stemming reduces precision , or TRUE negative rate , for such systems .", "entity": [{"entity": "precision", "entity_type": "metrics", "pos": [31, 40]}, {"entity": "TRUE negative rate", "entity_type": "metrics", "pos": [46, 64]}], "task": "NER"}
{"text": "A special case of keyword spotting is wake word ( also called hot word ) detection used by personal digital assistants such as Alexa or Siri to wake up when their name is spoken .", "entity": [{"entity": "keyword spotting", "entity_type": "task", "pos": [18, 34]}, {"entity": "wake word", "entity_type": "else", "pos": [38, 47]}, {"entity": "hot word", "entity_type": "else", "pos": [62, 70]}, {"entity": "Alexa", "entity_type": "product", "pos": [127, 132]}, {"entity": "Siri", "entity_type": "product", "pos": [136, 140]}], "task": "NER"}
{"text": "Prova is an open source programming language that combines Prolog with Java .", "entity": [{"entity": "Prova", "entity_type": "program language", "pos": [0, 5]}, {"entity": "Prolog", "entity_type": "program language", "pos": [59, 65]}, {"entity": "Java", "entity_type": "program language", "pos": [71, 75]}], "task": "NER"}
{"text": "In 1987 , Tocibai Machine , a subsidiary of Toshiba , was accused of illegally selling CNC milling s used to produce very quiet submarine propellers to the Soviet Union in violation of the CoCom agreement , an international embargo on certain countries to COMECON countries .", "entity": [{"entity": "Tocibai Machine", "entity_type": "organization", "pos": [10, 25]}, {"entity": "Toshiba", "entity_type": "organization", "pos": [44, 51]}, {"entity": "CNC milling", "entity_type": "product", "pos": [87, 98]}, {"entity": "Soviet Union", "entity_type": "country", "pos": [156, 168]}, {"entity": "CoCom", "entity_type": "organization", "pos": [189, 194]}, {"entity": "COMECON", "entity_type": "else", "pos": [256, 263]}], "task": "NER"}
{"text": "Engelberger 's most famous co-invention , the Unimate industrial robotic arm , was among the first inductees into the Robot Hall of Fame in 2003 .", "entity": [{"entity": "Engelberger", "entity_type": "researcher", "pos": [0, 11]}, {"entity": "Unimate industrial robotic arm", "entity_type": "product", "pos": [46, 76]}, {"entity": "Robot Hall of Fame", "entity_type": "location", "pos": [118, 136]}], "task": "NER"}
{"text": "Originally controlled via static html web pages using CGI , work by Dalton saw the introduction of an augmented reality Java -based interface that met with limited success .", "entity": [{"entity": "static html", "entity_type": "else", "pos": [26, 37]}, {"entity": "CGI", "entity_type": "else", "pos": [54, 57]}, {"entity": "Dalton", "entity_type": "person", "pos": [68, 74]}, {"entity": "augmented reality", "entity_type": "field", "pos": [102, 119]}, {"entity": "Java", "entity_type": "program language", "pos": [120, 124]}], "task": "NER"}
{"text": "The first publication about the LMF specification as it has been ratified by ISO ( this paper became ( in 2015 ) the 9th most cited paper within the LREC conferences from LREC papers ) :", "entity": [{"entity": "LMF specification", "entity_type": "task", "pos": [32, 49]}, {"entity": "ISO", "entity_type": "organization", "pos": [77, 80]}, {"entity": "LREC", "entity_type": "conference", "pos": [149, 153]}, {"entity": "LREC", "entity_type": "conference", "pos": [171, 175]}], "task": "NER"}
{"text": "A confusion matrix or matching matrix is often used as a tool to validate the accuracy of k -NN classification .", "entity": [{"entity": "confusion matrix", "entity_type": "metrics", "pos": [2, 18]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [78, 86]}, {"entity": "k -NN classification", "entity_type": "algorithm", "pos": [90, 110]}], "task": "NER"}
{"text": "Decision tree learning is one of the predictive modeling approaches used in statistics , data mining and machine learning .", "entity": [{"entity": "Decision tree", "entity_type": "algorithm", "pos": [0, 13]}, {"entity": "statistics", "entity_type": "field", "pos": [76, 86]}, {"entity": "data mining", "entity_type": "field", "pos": [89, 100]}, {"entity": "machine learning", "entity_type": "field", "pos": [105, 121]}], "task": "NER"}
{"text": "At runtime , the target prosody of a sentence is superimposed on these minimal units by means of signal processing techniques such as linear predictive coding , PSOLA", "entity": [{"entity": "prosody", "entity_type": "else", "pos": [24, 31]}, {"entity": "signal processing", "entity_type": "field", "pos": [97, 114]}, {"entity": "linear predictive coding", "entity_type": "algorithm", "pos": [134, 158]}], "task": "NER"}
{"text": "This approach utilized artificial intelligence and machine learning to allow researchers to visibly compare conventional and thermal facial imagery .", "entity": [{"entity": "artificial intelligence", "entity_type": "field", "pos": [23, 46]}, {"entity": "machine learning", "entity_type": "field", "pos": [51, 67]}, {"entity": "facial imagery", "entity_type": "task", "pos": [133, 147]}], "task": "NER"}
{"text": "In computer science , evolutionary computation is a family of algorithms for global optimization inspired by biological evolution , and the subfield of artificial intelligence and soft computing studying these algorithms .", "entity": [{"entity": "computer science", "entity_type": "field", "pos": [3, 19]}, {"entity": "evolutionary computation", "entity_type": "algorithm", "pos": [22, 46]}, {"entity": "global optimization", "entity_type": "task", "pos": [77, 96]}, {"entity": "biological evolution", "entity_type": "else", "pos": [109, 129]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [152, 175]}, {"entity": "soft computing", "entity_type": "field", "pos": [180, 194]}], "task": "NER"}
{"text": "For instance , one can combine some measure based on the confusion matrix with the mean squared error evaluated between the raw model outputs and the actual values .", "entity": [{"entity": "confusion matrix", "entity_type": "metrics", "pos": [57, 73]}, {"entity": "mean squared error", "entity_type": "metrics", "pos": [83, 101]}], "task": "NER"}
{"text": "The majority are results of the word2vec model developed by Mikolov et al or variants of word2vec .", "entity": [{"entity": "word2vec model", "entity_type": "product", "pos": [32, 46]}, {"entity": "Mikolov", "entity_type": "researcher", "pos": [60, 67]}, {"entity": "word2vec", "entity_type": "product", "pos": [89, 97]}], "task": "NER"}
{"text": "It was during this time that a total of 43 publications were recognized by the CVPR and the International Conference on Computer Vision ( ICCV ) .", "entity": [{"entity": "CVPR", "entity_type": "conference", "pos": [79, 83]}, {"entity": "International Conference on Computer Vision", "entity_type": "conference", "pos": [92, 135]}, {"entity": "ICCV", "entity_type": "conference", "pos": [138, 142]}], "task": "NER"}
{"text": "The AIBO has seen much use as an inexpensive platform for artificial intelligence education and research , because integrates a computer , Computer vision , and articulators in a package vastly cheaper than conventional research robots .", "entity": [{"entity": "AIBO", "entity_type": "product", "pos": [4, 8]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [58, 81]}, {"entity": "Computer vision", "entity_type": "field", "pos": [139, 154]}], "task": "NER"}
{"text": "She served as Program Chair of International Conference on Computer Vision 2021 .", "entity": [{"entity": "International Conference on Computer Vision 2021", "entity_type": "conference", "pos": [31, 79]}], "task": "NER"}
{"text": "Scheinman , after receiving a fellowship from Unimation to develop his designs , sold those designs to Unimation who further developed them with support from General Motors and later marketed it as the Programmable Universal Machine for Assembly ( PUMA ) .", "entity": [{"entity": "Scheinman", "entity_type": "researcher", "pos": [0, 9]}, {"entity": "Unimation", "entity_type": "organization", "pos": [46, 55]}, {"entity": "Unimation", "entity_type": "organization", "pos": [103, 112]}, {"entity": "General Motors", "entity_type": "organization", "pos": [158, 172]}, {"entity": "Programmable Universal Machine for Assembly", "entity_type": "product", "pos": [202, 245]}, {"entity": "PUMA", "entity_type": "product", "pos": [248, 252]}], "task": "NER"}
{"text": "An overview of calibration methods for binary classification and multiclass classification classification tasks is given by Gebel ( 2009 )", "entity": [{"entity": "binary classification", "entity_type": "task", "pos": [39, 60]}, {"entity": "multiclass classification classification tasks", "entity_type": "task", "pos": [65, 111]}, {"entity": "Gebel", "entity_type": "researcher", "pos": [124, 129]}], "task": "NER"}
{"text": "He is involved in fields such as optical character recognition ( OCR ) , speech synthesis , speech recognition technology , and electronic keyboard instruments .", "entity": [{"entity": "optical character recognition", "entity_type": "task", "pos": [33, 62]}, {"entity": "OCR", "entity_type": "task", "pos": [65, 68]}, {"entity": "speech synthesis", "entity_type": "task", "pos": [73, 89]}, {"entity": "speech recognition", "entity_type": "task", "pos": [92, 110]}], "task": "NER"}
{"text": "For more recent and state-of-the-art techniques , Kaldi toolkit can be used .", "entity": [{"entity": "Kaldi toolkit", "entity_type": "product", "pos": [50, 63]}], "task": "NER"}
{"text": "Johnson-Laird is a Fellow of the American Philosophical Society , a Fellow of the Royal Society , a Fellow of the British Academy , a William James Fellow of the Association for Psychological Science , and a Fellow of the Cognitive Science Society .", "entity": [{"entity": "Johnson-Laird", "entity_type": "researcher", "pos": [0, 13]}, {"entity": "American Philosophical Society", "entity_type": "organization", "pos": [33, 63]}, {"entity": "Royal Society", "entity_type": "organization", "pos": [82, 95]}, {"entity": "British Academy", "entity_type": "organization", "pos": [114, 129]}, {"entity": "William James", "entity_type": "researcher", "pos": [134, 147]}, {"entity": "Association for Psychological Science", "entity_type": "organization", "pos": [162, 199]}, {"entity": "Cognitive Science Society", "entity_type": "organization", "pos": [222, 247]}], "task": "NER"}
{"text": "At the IEEE International Conference on Image Processing in 2010 , Rui Hu , Mark Banard , and John Collomosse extended the HOG descriptor for use in sketch based image retrieval ( SBIR ) .", "entity": [{"entity": "IEEE International Conference on Image Processing", "entity_type": "conference", "pos": [7, 56]}, {"entity": "Rui Hu", "entity_type": "researcher", "pos": [67, 73]}, {"entity": "Mark Banard", "entity_type": "researcher", "pos": [76, 87]}, {"entity": "John Collomosse", "entity_type": "researcher", "pos": [94, 109]}, {"entity": "HOG descriptor", "entity_type": "algorithm", "pos": [123, 137]}, {"entity": "sketch based image retrieval", "entity_type": "task", "pos": [149, 177]}, {"entity": "SBIR", "entity_type": "task", "pos": [180, 184]}], "task": "NER"}
{"text": "BLEU uses a modified form of precision to compare a candidate translation against multiple reference translations .", "entity": [{"entity": "BLEU", "entity_type": "metrics", "pos": [0, 4]}, {"entity": "precision", "entity_type": "metrics", "pos": [29, 38]}], "task": "NER"}
{"text": "For the case of a general base space math ( Y , \\ mathcal { B } , \\ nu ) / math ( i.e. a base space which is not countable ) , one typically considers the relative entropy .", "entity": [{"entity": "relative entropy", "entity_type": "metrics", "pos": [155, 171]}], "task": "NER"}
{"text": "As of October 2011 , the already-existing partnerships with the United States ' National Park Service ( NPS ) , the United Kingdom 's Historic Scotland ( HS ) , World Monuments Fund , and Mexico 's Instituto Nacional de Antropología y Historia ( INAH ) had been greatly expanded , , CyArk website", "entity": [{"entity": "United States", "entity_type": "country", "pos": [64, 77]}, {"entity": "National Park Service", "entity_type": "organization", "pos": [80, 101]}, {"entity": "NPS", "entity_type": "organization", "pos": [104, 107]}, {"entity": "United Kingdom", "entity_type": "country", "pos": [116, 130]}, {"entity": "Historic Scotland", "entity_type": "organization", "pos": [134, 151]}, {"entity": "HS", "entity_type": "organization", "pos": [154, 156]}, {"entity": "World Monuments Fund", "entity_type": "organization", "pos": [161, 181]}, {"entity": "Mexico", "entity_type": "country", "pos": [188, 194]}, {"entity": "Instituto Nacional de Antropología y Historia", "entity_type": "organization", "pos": [198, 243]}, {"entity": "INAH", "entity_type": "organization", "pos": [246, 250]}, {"entity": "CyArk", "entity_type": "else", "pos": [283, 288]}], "task": "NER"}
{"text": "Kernel SVMs are available in many machine-learning toolkits , including LIBSVM , MATLAB , and others .", "entity": [{"entity": "Kernel SVMs", "entity_type": "algorithm", "pos": [0, 11]}, {"entity": "machine-learning", "entity_type": "field", "pos": [34, 50]}, {"entity": "LIBSVM", "entity_type": "product", "pos": [72, 78]}, {"entity": "MATLAB", "entity_type": "product", "pos": [81, 87]}], "task": "NER"}
{"text": "The 2009 Loebner Prize Competition was held September 6 , 2009 at the Brighton Centre , Brighton UK in conjunction with the Interspeech 2009 conference .", "entity": [{"entity": "Loebner Prize Competition", "entity_type": "else", "pos": [9, 34]}, {"entity": "Brighton Centre", "entity_type": "location", "pos": [70, 85]}, {"entity": "Brighton", "entity_type": "location", "pos": [88, 96]}, {"entity": "UK", "entity_type": "country", "pos": [97, 99]}, {"entity": "Interspeech 2009 conference", "entity_type": "conference", "pos": [124, 151]}], "task": "NER"}
{"text": "The humanoid QRIO robot was designed as the successor to AIBO , and runs the same base R-CODE Aperios operating system .", "entity": [{"entity": "QRIO robot", "entity_type": "product", "pos": [13, 23]}, {"entity": "AIBO", "entity_type": "product", "pos": [57, 61]}, {"entity": "R-CODE", "entity_type": "product", "pos": [87, 93]}, {"entity": "Aperios operating system", "entity_type": "product", "pos": [94, 118]}], "task": "NER"}
{"text": "Speech waveforms are generated from HMMs themselves based on the maximum likelihood criterion .", "entity": [{"entity": "Speech waveforms", "entity_type": "else", "pos": [0, 16]}, {"entity": "HMMs", "entity_type": "algorithm", "pos": [36, 40]}, {"entity": "maximum likelihood", "entity_type": "algorithm", "pos": [65, 83]}], "task": "NER"}
{"text": "Google Translate is a free multilingual statistical machine translation and neural machine translation service developed by Google , to translate text and websites from one language into another .", "entity": [{"entity": "Google Translate", "entity_type": "product", "pos": [0, 16]}, {"entity": "multilingual statistical machine translation", "entity_type": "task", "pos": [27, 71]}, {"entity": "neural machine translation", "entity_type": "task", "pos": [76, 102]}, {"entity": "Google", "entity_type": "product", "pos": [124, 130]}], "task": "NER"}
{"text": "Skeletons are widely used in computer vision , image analysis , pattern recognition and digital image processing for purposes such as optical character recognition , fingerprint recognition , visual inspection or compression .", "entity": [{"entity": "computer vision", "entity_type": "field", "pos": [29, 44]}, {"entity": "image analysis", "entity_type": "field", "pos": [47, 61]}, {"entity": "pattern recognition", "entity_type": "field", "pos": [64, 83]}, {"entity": "digital image processing", "entity_type": "field", "pos": [88, 112]}, {"entity": "optical character recognition", "entity_type": "task", "pos": [134, 163]}, {"entity": "fingerprint recognition", "entity_type": "task", "pos": [166, 189]}, {"entity": "visual inspection or compression", "entity_type": "task", "pos": [192, 224]}], "task": "NER"}
{"text": "The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object classification and detection , with millions of images and hundreds of object classes .", "entity": [{"entity": "ImageNet Large Scale Visual Recognition Challenge", "entity_type": "conference", "pos": [4, 53]}, {"entity": "object classification and detection", "entity_type": "task", "pos": [72, 107]}], "task": "NER"}
{"text": "Bengio , together with Geoffrey Hinton and Yann LeCun , are referred to by some as the Godfathers of AI and Godfathers of Deep Learning .", "entity": [{"entity": "Bengio", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "Geoffrey Hinton", "entity_type": "researcher", "pos": [23, 38]}, {"entity": "Yann LeCun", "entity_type": "researcher", "pos": [43, 53]}, {"entity": "Godfathers of AI", "entity_type": "else", "pos": [87, 103]}, {"entity": "Godfathers of Deep Learning", "entity_type": "else", "pos": [108, 135]}], "task": "NER"}
{"text": "He is a Life Fellow of IEEE .", "entity": [{"entity": "IEEE", "entity_type": "organization", "pos": [23, 27]}], "task": "NER"}
{"text": "NSA Bethesda is responsible for base operational support for its major tenant , the Walter Reed National Military Medical Center .", "entity": [{"entity": "NSA Bethesda", "entity_type": "organization", "pos": [0, 12]}, {"entity": "Walter Reed National Military Medical Center", "entity_type": "organization", "pos": [84, 128]}], "task": "NER"}
{"text": "The three major learning paradigms are supervised learning , unsupervised learning and reinforcement learning .", "entity": [{"entity": "supervised learning", "entity_type": "field", "pos": [39, 58]}, {"entity": "unsupervised learning", "entity_type": "field", "pos": [61, 82]}, {"entity": "reinforcement learning", "entity_type": "field", "pos": [87, 109]}], "task": "NER"}
{"text": "Examples include control , planning and scheduling , the ability to answer diagnostic and consumer questions , handwriting recognition , natural language understanding , speech recognition and facial recognition .", "entity": [{"entity": "control", "entity_type": "task", "pos": [17, 24]}, {"entity": "planning and scheduling", "entity_type": "task", "pos": [27, 50]}, {"entity": "answer diagnostic and consumer questions", "entity_type": "task", "pos": [68, 108]}, {"entity": "handwriting recognition", "entity_type": "task", "pos": [111, 134]}, {"entity": "natural language understanding", "entity_type": "task", "pos": [137, 167]}, {"entity": "speech recognition", "entity_type": "task", "pos": [170, 188]}, {"entity": "facial recognition", "entity_type": "task", "pos": [193, 211]}], "task": "NER"}
{"text": "In 1991 he was elected as a fellow of the Association for the Advancement of Artificial Intelligence ( 1990 , founding fellow ) .", "entity": [{"entity": "Association for the Advancement of Artificial Intelligence", "entity_type": "conference", "pos": [42, 100]}], "task": "NER"}
{"text": "However , by formulating the problem as the solution of a Toeplitz matrix and using Levinson recursion , we can relatively quickly estimate a filter with the smallest mean squared error possible .", "entity": [{"entity": "Toeplitz matrix", "entity_type": "else", "pos": [58, 73]}, {"entity": "Levinson recursion", "entity_type": "algorithm", "pos": [84, 102]}, {"entity": "mean squared error", "entity_type": "metrics", "pos": [167, 185]}], "task": "NER"}
{"text": "In July 2011 the 15th edition of Campus Party Spain will be held at the City of Arts and Sciences in Valencia .", "entity": [{"entity": "15th edition of Campus Party Spain", "entity_type": "conference", "pos": [17, 51]}, {"entity": "City of Arts and Sciences", "entity_type": "location", "pos": [72, 97]}, {"entity": "Valencia", "entity_type": "location", "pos": [101, 109]}], "task": "NER"}
{"text": "Often this is generally only possible at the very end of complicated games such as chess or go , since it is not computationally feasible to look ahead as far as the completion of the game , except towards the end , and instead , positions are given finite values as estimates of the degree of belief that they will lead to a win for one player or another .", "entity": [{"entity": "chess", "entity_type": "product", "pos": [83, 88]}, {"entity": "go", "entity_type": "product", "pos": [92, 94]}], "task": "NER"}
{"text": "The difference between the multinomial logit model and numerous other methods , models , algorithms , etc. with the same basic setup ( the perceptron algorithm , support vector machine s , linear discriminant analysis , etc .", "entity": [{"entity": "multinomial logit model", "entity_type": "algorithm", "pos": [27, 50]}, {"entity": "perceptron algorithm", "entity_type": "algorithm", "pos": [139, 159]}, {"entity": "support vector machine", "entity_type": "algorithm", "pos": [162, 184]}, {"entity": "linear discriminant analysis", "entity_type": "algorithm", "pos": [189, 217]}], "task": "NER"}
{"text": "Association for Computational Linguistics , published by", "entity": [{"entity": "Association for Computational Linguistics", "entity_type": "conference", "pos": [0, 41]}], "task": "NER"}
{"text": "In computerised Facial recognition system , each face is represented by a large number of pixel values .", "entity": [{"entity": "Facial recognition system", "entity_type": "product", "pos": [16, 41]}], "task": "NER"}
{"text": "In 2002 , his son , Daniel Pearl , a journalist working for the Wall Street Journal was kidnapped and murdered in Pakistan , leading Judea and the other members of the family and friends to create the Daniel Pearl Foundation .", "entity": [{"entity": "Daniel Pearl", "entity_type": "person", "pos": [20, 32]}, {"entity": "Wall Street Journal", "entity_type": "organization", "pos": [64, 83]}, {"entity": "Pakistan", "entity_type": "country", "pos": [114, 122]}, {"entity": "Judea", "entity_type": "person", "pos": [133, 138]}, {"entity": "Daniel Pearl Foundation", "entity_type": "organization", "pos": [201, 224]}], "task": "NER"}
{"text": "As of late 2006 , Red Envelope Entertainment also expanded into producing original content with filmmakers such as John Waters .", "entity": [{"entity": "Red Envelope Entertainment", "entity_type": "organization", "pos": [18, 44]}, {"entity": "John Waters", "entity_type": "person", "pos": [115, 126]}], "task": "NER"}
{"text": "The building is now part of the Beth Israel Deaconess Medical Center .", "entity": [{"entity": "Beth Israel Deaconess Medical Center", "entity_type": "organization", "pos": [32, 68]}], "task": "NER"}
{"text": "A common theme of this work is the adoption of a sign-theoretic perspective on issues of artificial intelligence and knowledge representation .", "entity": [{"entity": "artificial intelligence", "entity_type": "field", "pos": [89, 112]}, {"entity": "knowledge representation", "entity_type": "task", "pos": [117, 141]}], "task": "NER"}
{"text": "For instance , the term neural machine translation ( NMT ) emphasizes the fact that deep learning-based approaches to machine translation directly learn sequence-to-sequence transformations , obviating the need for intermediate steps such as word alignment and language modeling that was used in statistical machine translation ( SMT ) .", "entity": [{"entity": "neural machine translation", "entity_type": "task", "pos": [24, 50]}, {"entity": "NMT", "entity_type": "task", "pos": [53, 56]}, {"entity": "machine translation", "entity_type": "task", "pos": [118, 137]}, {"entity": "word alignment", "entity_type": "task", "pos": [242, 256]}, {"entity": "language modeling", "entity_type": "task", "pos": [261, 278]}, {"entity": "statistical machine translation", "entity_type": "task", "pos": [296, 327]}, {"entity": "SMT", "entity_type": "task", "pos": [330, 333]}], "task": "NER"}
{"text": "Most research in the field of WSD is performed by using WordNet as a reference sense inventory for .", "entity": [{"entity": "WSD", "entity_type": "field", "pos": [30, 33]}, {"entity": "WordNet", "entity_type": "product", "pos": [56, 63]}], "task": "NER"}
{"text": "Notable former PhD students and postdoctoral researchers from his group include Richard Zemel , and Zoubin Ghahramani .", "entity": [{"entity": "PhD", "entity_type": "else", "pos": [15, 18]}, {"entity": "Richard Zemel", "entity_type": "researcher", "pos": [80, 93]}, {"entity": "Zoubin Ghahramani", "entity_type": "researcher", "pos": [100, 117]}], "task": "NER"}
{"text": "Each prediction result or instance of a confusion matrix represents one point in the ROC space .", "entity": [{"entity": "confusion matrix", "entity_type": "metrics", "pos": [40, 56]}, {"entity": "ROC", "entity_type": "metrics", "pos": [85, 88]}], "task": "NER"}
{"text": "In 1997 Thrun and his colleagues Wolfram Burgard and Dieter Fox developed the world 's first robotic tour guide in the Deutsches Museum Bonn ( 1997 ) .", "entity": [{"entity": "Thrun", "entity_type": "researcher", "pos": [8, 13]}, {"entity": "Wolfram Burgard", "entity_type": "researcher", "pos": [33, 48]}, {"entity": "Dieter Fox", "entity_type": "researcher", "pos": [53, 63]}, {"entity": "robotic tour guide", "entity_type": "product", "pos": [93, 111]}, {"entity": "Deutsches Museum Bonn", "entity_type": "location", "pos": [119, 140]}], "task": "NER"}
{"text": "WordNet is a lexical database of semantic relation s between word s in more than 200 languages. its primary use is in automatic natural language processing and artificial intelligence applications .", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [0, 7]}, {"entity": "semantic relation", "entity_type": "else", "pos": [33, 50]}, {"entity": "natural language processing", "entity_type": "field", "pos": [128, 155]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [160, 183]}], "task": "NER"}
{"text": "Conferences in the field of natural language processing , such as Association for Computational Linguistics , North American Chapter of the Association for Computational Linguistics , EMNLP , and HLT , are beginning to include papers on speech processing .", "entity": [{"entity": "natural language processing", "entity_type": "field", "pos": [28, 55]}, {"entity": "Association for Computational Linguistics", "entity_type": "conference", "pos": [66, 107]}, {"entity": "North American Chapter of the Association for Computational Linguistics", "entity_type": "conference", "pos": [110, 181]}, {"entity": "EMNLP", "entity_type": "conference", "pos": [184, 189]}, {"entity": "HLT", "entity_type": "conference", "pos": [196, 199]}, {"entity": "speech processing", "entity_type": "field", "pos": [237, 254]}], "task": "NER"}
{"text": "A set of Java programs use the lexicon to work through the variations in biomedical texts by relating words by their parts of speech , which can be helpful in web searches or searches through an electronic medical record .", "entity": [{"entity": "Java", "entity_type": "program language", "pos": [9, 13]}, {"entity": "parts of speech", "entity_type": "else", "pos": [117, 132]}, {"entity": "electronic medical record", "entity_type": "else", "pos": [195, 220]}], "task": "NER"}
{"text": "There are many more recent algorithms such as LPBoost , TotalBoost , BrownBoost , xgboost , MadaBoost , , and others .", "entity": [{"entity": "LPBoost", "entity_type": "algorithm", "pos": [46, 53]}, {"entity": "TotalBoost", "entity_type": "algorithm", "pos": [56, 66]}, {"entity": "BrownBoost", "entity_type": "algorithm", "pos": [69, 79]}, {"entity": "xgboost", "entity_type": "algorithm", "pos": [82, 89]}, {"entity": "MadaBoost", "entity_type": "algorithm", "pos": [92, 101]}], "task": "NER"}
{"text": "This is an example implementation in Python :", "entity": [{"entity": "Python", "entity_type": "program language", "pos": [37, 43]}], "task": "NER"}
{"text": "The Mattel Intellivision game console offered the Intellivoice Voice Synthesis module in 1982 .", "entity": [{"entity": "Mattel", "entity_type": "product", "pos": [4, 10]}, {"entity": "Intellivision", "entity_type": "product", "pos": [11, 24]}, {"entity": "Intellivoice Voice Synthesis", "entity_type": "task", "pos": [50, 78]}], "task": "NER"}
{"text": "He also worked on machine translation , both high-accuracy knowledge-based MT and machine learning for Statistical machine translation ( such as generalized example-based MT ) .", "entity": [{"entity": "machine translation", "entity_type": "task", "pos": [18, 37]}, {"entity": "high-accuracy knowledge-based MT", "entity_type": "task", "pos": [45, 77]}, {"entity": "machine learning", "entity_type": "field", "pos": [82, 98]}, {"entity": "Statistical machine translation", "entity_type": "task", "pos": [103, 134]}, {"entity": "generalized example-based MT", "entity_type": "task", "pos": [145, 173]}], "task": "NER"}
{"text": "Wolfram Mathematica ( usually termed Mathematica ) is a modern technical computing system spanning most areas of technical - including neural networks , machine learning , image processing , geometry , data science , visualizations , and others .", "entity": [{"entity": "Wolfram Mathematica", "entity_type": "organization", "pos": [0, 19]}, {"entity": "Mathematica", "entity_type": "organization", "pos": [37, 48]}, {"entity": "neural networks", "entity_type": "algorithm", "pos": [135, 150]}, {"entity": "machine learning", "entity_type": "field", "pos": [153, 169]}, {"entity": "image processing", "entity_type": "field", "pos": [172, 188]}, {"entity": "geometry", "entity_type": "field", "pos": [191, 199]}, {"entity": "data science", "entity_type": "field", "pos": [202, 214]}, {"entity": "visualizations", "entity_type": "field", "pos": [217, 231]}], "task": "NER"}
{"text": "The first digitally operated and programmable robot was invented by George Devol in 1954 and was ultimately called the Unimate .", "entity": [{"entity": "digitally operated and programmable robot", "entity_type": "product", "pos": [10, 51]}, {"entity": "George Devol", "entity_type": "researcher", "pos": [68, 80]}, {"entity": "Unimate", "entity_type": "product", "pos": [119, 126]}], "task": "NER"}
{"text": "Like DBNs , DBMs can learn complex and abstract internal representations of the input in tasks such as Object recognition or speech recognition , using limited , labeled data to fine-tune the representations built using a large set of unlabeled sensory input data .", "entity": [{"entity": "DBNs", "entity_type": "algorithm", "pos": [5, 9]}, {"entity": "DBMs", "entity_type": "algorithm", "pos": [12, 16]}, {"entity": "Object recognition", "entity_type": "task", "pos": [103, 121]}, {"entity": "speech recognition", "entity_type": "task", "pos": [125, 143]}], "task": "NER"}
{"text": "Scientific conferences where vision based activity recognition work often appears are ICCV and CVPR .", "entity": [{"entity": "vision based activity recognition", "entity_type": "task", "pos": [29, 62]}, {"entity": "ICCV", "entity_type": "conference", "pos": [86, 90]}, {"entity": "CVPR", "entity_type": "conference", "pos": [95, 99]}], "task": "NER"}
{"text": "In statistics , an expectation-maximization ( EM ) algorithm is an iterative method to find maximum likelihood or maximum a posteriori ( MAP ) estimates of parameter s in statistical model s , where the model depends on unobserved latent variable s .", "entity": [{"entity": "statistics", "entity_type": "field", "pos": [3, 13]}, {"entity": "expectation-maximization", "entity_type": "algorithm", "pos": [19, 43]}, {"entity": "EM", "entity_type": "algorithm", "pos": [46, 48]}, {"entity": "maximum likelihood", "entity_type": "metrics", "pos": [92, 110]}, {"entity": "maximum a posteriori", "entity_type": "metrics", "pos": [114, 134]}, {"entity": "MAP", "entity_type": "metrics", "pos": [137, 140]}, {"entity": "latent variable", "entity_type": "else", "pos": [231, 246]}], "task": "NER"}
{"text": "Similarly , investigators sometimes report the FALSE Positive Rate ( FPR ) as well as the FALSE Negative Rate ( FNR ) .", "entity": [{"entity": "FALSE Positive Rate", "entity_type": "metrics", "pos": [47, 66]}, {"entity": "FPR", "entity_type": "metrics", "pos": [69, 72]}, {"entity": "FALSE Negative Rate", "entity_type": "metrics", "pos": [90, 109]}, {"entity": "FNR", "entity_type": "metrics", "pos": [112, 115]}], "task": "NER"}
{"text": "The concept is similar to the signal to noise ratio used in the sciences and confusion matrix used in artificial intelligence .", "entity": [{"entity": "signal to noise ratio", "entity_type": "metrics", "pos": [30, 51]}, {"entity": "sciences", "entity_type": "field", "pos": [64, 72]}, {"entity": "confusion matrix", "entity_type": "metrics", "pos": [77, 93]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [102, 125]}], "task": "NER"}
{"text": "The Code of Ethics on Human Augmentation , which was originally introduced by Steve Mann in 2004 and refined with Ray Kurzweil and Marvin Minsky in 2013 , was ultimately ratified at the Virtual Reality Toronto conference on June 25 , 2017 .", "entity": [{"entity": "Human Augmentation", "entity_type": "field", "pos": [22, 40]}, {"entity": "Steve Mann", "entity_type": "researcher", "pos": [78, 88]}, {"entity": "Ray Kurzweil", "entity_type": "researcher", "pos": [114, 126]}, {"entity": "Marvin Minsky", "entity_type": "researcher", "pos": [131, 144]}, {"entity": "Virtual Reality Toronto conference", "entity_type": "conference", "pos": [186, 220]}], "task": "NER"}
{"text": "In 1913 , Walter R. Booth directed 10 films for the U.K. Kinoplastikon , presumably in collaboration with Cecil Hepworth .", "entity": [{"entity": "Walter R. Booth", "entity_type": "person", "pos": [10, 25]}, {"entity": "U.K. Kinoplastikon", "entity_type": "organization", "pos": [52, 70]}, {"entity": "Cecil Hepworth", "entity_type": "person", "pos": [106, 120]}], "task": "NER"}
{"text": "They introduced their new robot in 1961 at a trade show at Chicago 's Cow Palace .", "entity": [{"entity": "Chicago", "entity_type": "location", "pos": [59, 66]}, {"entity": "Cow Palace", "entity_type": "location", "pos": [70, 80]}], "task": "NER"}
{"text": "While some chatbot applications use extensive word-classification processes , natural language processing processors , and sophisticated Artificial intelligence , others simply scan for general keywords and generate responses using common phrases obtained from an associated library or database .", "entity": [{"entity": "chatbot", "entity_type": "product", "pos": [11, 18]}, {"entity": "word-classification", "entity_type": "task", "pos": [46, 65]}, {"entity": "natural language processing", "entity_type": "field", "pos": [78, 105]}, {"entity": "Artificial intelligence", "entity_type": "field", "pos": [137, 160]}], "task": "NER"}
{"text": "The WaveNet model proposed in 2016 achieves great performance on speech quality .", "entity": [{"entity": "WaveNet", "entity_type": "product", "pos": [4, 11]}], "task": "NER"}
{"text": "Organizations known to use ALE for Emergency management , disaster relief , ordinary communication or extraordinary situation response : American Red Cross , FEMA , Disaster Medical Assistance Team s , NATO , Federal Bureau of Investigation , United Nations , AT & T , Civil Air Patrol , ( ARES ) .", "entity": [{"entity": "ALE", "entity_type": "product", "pos": [27, 30]}, {"entity": "Emergency management", "entity_type": "else", "pos": [35, 55]}, {"entity": "disaster relief", "entity_type": "else", "pos": [58, 73]}, {"entity": "ordinary communication", "entity_type": "else", "pos": [76, 98]}, {"entity": "extraordinary situation response", "entity_type": "else", "pos": [102, 134]}, {"entity": "American Red Cross", "entity_type": "organization", "pos": [137, 155]}, {"entity": "FEMA", "entity_type": "organization", "pos": [158, 162]}, {"entity": "Disaster Medical Assistance Team", "entity_type": "organization", "pos": [165, 197]}, {"entity": "NATO", "entity_type": "organization", "pos": [202, 206]}, {"entity": "Federal Bureau of Investigation", "entity_type": "organization", "pos": [209, 240]}, {"entity": "United Nations", "entity_type": "organization", "pos": [243, 257]}, {"entity": "AT & T", "entity_type": "organization", "pos": [260, 266]}, {"entity": "Civil Air Patrol", "entity_type": "organization", "pos": [269, 285]}, {"entity": "ARES", "entity_type": "organization", "pos": [290, 294]}], "task": "NER"}
{"text": "Here , the Kronecker delta is used for simplicity ( cf. the derivative of a sigmoid function , being expressed via the function itself ) .", "entity": [{"entity": "Kronecker delta", "entity_type": "algorithm", "pos": [11, 26]}, {"entity": "sigmoid function", "entity_type": "algorithm", "pos": [76, 92]}], "task": "NER"}
{"text": "The theory is based in philosophical foundations , and was founded by Ray Solomonoff around 1960 . Samuel Rathmanner and Marcus Hutter .", "entity": [{"entity": "Ray Solomonoff", "entity_type": "researcher", "pos": [70, 84]}, {"entity": "Samuel Rathmanner", "entity_type": "researcher", "pos": [99, 116]}, {"entity": "Marcus Hutter", "entity_type": "researcher", "pos": [121, 134]}], "task": "NER"}
{"text": "WordNet , a freely available database originally designed as a semantic network based on psycholinguistic principles , was expanded by addition of definitions and is now also viewed as a dictionary .", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [0, 7]}, {"entity": "semantic network", "entity_type": "else", "pos": [63, 79]}, {"entity": "psycholinguistic principles", "entity_type": "else", "pos": [89, 116]}], "task": "NER"}
{"text": "Advances in the field of computational imaging research is presented in several venues including publications of SIGGRAPH and the .", "entity": [{"entity": "computational imaging", "entity_type": "field", "pos": [25, 46]}, {"entity": "SIGGRAPH", "entity_type": "conference", "pos": [113, 121]}], "task": "NER"}
{"text": "Classification can be thought of as two separate problems - binary classification and multiclass classification .", "entity": [{"entity": "Classification", "entity_type": "task", "pos": [0, 14]}, {"entity": "binary classification", "entity_type": "task", "pos": [60, 81]}, {"entity": "multiclass classification", "entity_type": "task", "pos": [86, 111]}], "task": "NER"}
{"text": "Advanced gene finders for both prokaryotic and eukaryotic genomes typically use complex probabilistic model s , such as hidden Markov model s ( HMMs ) to combine information from a variety of different signal and content measurements .", "entity": [{"entity": "probabilistic model", "entity_type": "algorithm", "pos": [88, 107]}, {"entity": "hidden Markov model", "entity_type": "algorithm", "pos": [120, 139]}, {"entity": "HMMs", "entity_type": "algorithm", "pos": [144, 148]}], "task": "NER"}
{"text": "Neuroevolution , or neuro-evolution , is a form of artificial intelligence that uses evolutionary algorithm s to generate artificial neural network s ( ANN ) , parameters , topology and rules. and evolutionary robotics .", "entity": [{"entity": "Neuroevolution", "entity_type": "else", "pos": [0, 14]}, {"entity": "neuro-evolution", "entity_type": "else", "pos": [20, 35]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [51, 74]}, {"entity": "evolutionary algorithm", "entity_type": "algorithm", "pos": [85, 107]}, {"entity": "artificial neural network", "entity_type": "algorithm", "pos": [122, 147]}, {"entity": "ANN", "entity_type": "algorithm", "pos": [152, 155]}, {"entity": "evolutionary robotics", "entity_type": "algorithm", "pos": [197, 218]}], "task": "NER"}
{"text": "Since IBM proposed and realized the system of BLEU Papineni et al .", "entity": [{"entity": "IBM", "entity_type": "organization", "pos": [6, 9]}, {"entity": "BLEU", "entity_type": "metrics", "pos": [46, 50]}, {"entity": "Papineni", "entity_type": "researcher", "pos": [51, 59]}], "task": "NER"}
{"text": "In 2009 , experts attended a conference hosted by the Association for the Advancement of Artificial Intelligence ( AAAI ) to discuss whether computers and robots might be able to acquire any autonomy , and how much these abilities might pose a threat or hazard .", "entity": [{"entity": "Association for the Advancement of Artificial Intelligence", "entity_type": "conference", "pos": [54, 112]}, {"entity": "AAAI", "entity_type": "conference", "pos": [115, 119]}], "task": "NER"}
{"text": "After boosting , a classifier constructed from 200 features could yield a 95 % detection rate under a ^ { -5 } / math FALSE positive rate .P. Viola , M. Jones , Robust Real-time Object Detection , 2001 .", "entity": [{"entity": "FALSE positive rate", "entity_type": "metrics", "pos": [118, 137]}, {"entity": ".P. Viola", "entity_type": "researcher", "pos": [138, 147]}, {"entity": "M. Jones", "entity_type": "researcher", "pos": [150, 158]}, {"entity": "Robust Real-time Object Detection", "entity_type": "task", "pos": [161, 194]}], "task": "NER"}
{"text": "The website was originally Perl -based , but IMDb no longer discloses what software it uses for reasons of security .", "entity": [{"entity": "Perl", "entity_type": "program language", "pos": [27, 31]}, {"entity": "IMDb", "entity_type": "organization", "pos": [45, 49]}], "task": "NER"}
{"text": "The start-up was founded by Demis Hassabis , Shane Legg and Mustafa Suleyman in 2010 .", "entity": [{"entity": "Demis Hassabis", "entity_type": "researcher", "pos": [28, 42]}, {"entity": "Shane Legg", "entity_type": "researcher", "pos": [45, 55]}, {"entity": "Mustafa Suleyman", "entity_type": "person", "pos": [60, 76]}], "task": "NER"}
{"text": "Two very commonly used loss functions are the mean squared error , mathL ( a ) = a ^ 2 / math , and the absolute loss , mathL ( a ) = | a | / math .", "entity": [{"entity": "loss functions", "entity_type": "else", "pos": [23, 37]}, {"entity": "mean squared error", "entity_type": "metrics", "pos": [46, 64]}, {"entity": "absolute loss", "entity_type": "metrics", "pos": [104, 117]}], "task": "NER"}
{"text": "The soft-margin support vector machine described above is an example of an empirical risk minimization ( ERM ) for the hinge loss .", "entity": [{"entity": "support vector machine", "entity_type": "algorithm", "pos": [16, 38]}, {"entity": "empirical risk minimization", "entity_type": "algorithm", "pos": [75, 102]}, {"entity": "ERM", "entity_type": "algorithm", "pos": [105, 108]}, {"entity": "hinge loss", "entity_type": "metrics", "pos": [119, 129]}], "task": "NER"}
{"text": "A deep learning based approach to MT , neural machine translation has made rapid progress in recent years , and Google has announced its translation services are now using this technology in preference to its previous statistical methods .", "entity": [{"entity": "deep learning", "entity_type": "field", "pos": [2, 15]}, {"entity": "MT", "entity_type": "task", "pos": [34, 36]}, {"entity": "neural machine translation", "entity_type": "task", "pos": [39, 65]}, {"entity": "Google", "entity_type": "organization", "pos": [112, 118]}], "task": "NER"}
{"text": "This tends to yield very large performance gains when working with large corpora such as WordNet .", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [89, 96]}], "task": "NER"}
{"text": "Face detection is used in biometrics , often as a part of ( or together with ) a facial recognition system .", "entity": [{"entity": "Face detection", "entity_type": "task", "pos": [0, 14]}, {"entity": "biometrics", "entity_type": "field", "pos": [26, 36]}, {"entity": "facial recognition system", "entity_type": "product", "pos": [81, 106]}], "task": "NER"}
{"text": "trained by maximum likelihood estimation .", "entity": [{"entity": "maximum likelihood estimation", "entity_type": "algorithm", "pos": [11, 40]}], "task": "NER"}
{"text": ", Ltd. in Thailand ; Komatsu ( Shanghai ) Ltd. in 1996 in Shanghai , China ; Industrial Power Alliance Ltd. in Japan , a joint venture with Cummins , in 1998 ; L & T-Komatsu Limited in India in 1998 ( shares sold in 2013 ) ; and Komatsu Brasil International Ltda. in Brazil in 1998 .", "entity": [{"entity": "Thailand", "entity_type": "country", "pos": [10, 18]}, {"entity": "Komatsu ( Shanghai ) Ltd.", "entity_type": "organization", "pos": [21, 46]}, {"entity": "Shanghai", "entity_type": "location", "pos": [58, 66]}, {"entity": "China", "entity_type": "country", "pos": [69, 74]}, {"entity": "Industrial Power Alliance Ltd.", "entity_type": "organization", "pos": [77, 107]}, {"entity": "Japan", "entity_type": "country", "pos": [111, 116]}, {"entity": "Cummins", "entity_type": "organization", "pos": [140, 147]}, {"entity": "L & T-Komatsu Limited", "entity_type": "organization", "pos": [160, 181]}, {"entity": "India", "entity_type": "country", "pos": [185, 190]}, {"entity": "Komatsu Brasil International Ltda.", "entity_type": "organization", "pos": [229, 263]}, {"entity": "Brazil", "entity_type": "country", "pos": [267, 273]}], "task": "NER"}
{"text": "dgp also occasionally hosts artists in residence ( e.g. , Oscar -winner Chris Landreth .", "entity": [{"entity": "dgp", "entity_type": "organization", "pos": [0, 3]}, {"entity": "artists in residence", "entity_type": "else", "pos": [28, 48]}, {"entity": "Oscar", "entity_type": "else", "pos": [58, 63]}, {"entity": "Chris Landreth", "entity_type": "person", "pos": [72, 86]}], "task": "NER"}
{"text": "It currently includes four sub-competitions - the RoboMaster Robotics Competition , the RoboMaster Technical Challenge , the ICRA RoboMaster AI Challenge , and the new RoboMaster Youth Tournament .", "entity": [{"entity": "RoboMaster Robotics Competition", "entity_type": "else", "pos": [50, 81]}, {"entity": "RoboMaster Technical Challenge", "entity_type": "else", "pos": [88, 118]}, {"entity": "ICRA RoboMaster AI Challenge", "entity_type": "else", "pos": [125, 153]}, {"entity": "RoboMaster Youth Tournament", "entity_type": "else", "pos": [168, 195]}], "task": "NER"}
{"text": "By the early 2000s , the dominant speech processing strategy started to shift away from Hidden Markov model towards more modern neural networks and deep learning .", "entity": [{"entity": "speech processing", "entity_type": "field", "pos": [34, 51]}, {"entity": "Hidden Markov model", "entity_type": "algorithm", "pos": [88, 107]}, {"entity": "neural networks", "entity_type": "algorithm", "pos": [128, 143]}, {"entity": "deep learning", "entity_type": "field", "pos": [148, 161]}], "task": "NER"}
{"text": "Another equivalent expression , in the case of a binary target rate , is that the TRUE positive rate and the FALSE positive rate are equal ( and therefore the FALSE negative rate and the TRUE negative rate are equal ) for every value of the sensitive characteristics :", "entity": [{"entity": "binary target rate", "entity_type": "metrics", "pos": [49, 67]}, {"entity": "TRUE positive rate", "entity_type": "metrics", "pos": [82, 100]}, {"entity": "FALSE positive rate", "entity_type": "metrics", "pos": [109, 128]}, {"entity": "FALSE negative rate", "entity_type": "metrics", "pos": [159, 178]}, {"entity": "TRUE negative rate", "entity_type": "metrics", "pos": [187, 205]}], "task": "NER"}
{"text": "The MATLAB function ,", "entity": [{"entity": "MATLAB", "entity_type": "product", "pos": [4, 10]}], "task": "NER"}
{"text": "An articulated robot is a robot with rotary joint s ( e.g. a legged robot or an industrial robot ) .", "entity": [{"entity": "articulated robot", "entity_type": "product", "pos": [3, 20]}, {"entity": "rotary joint", "entity_type": "else", "pos": [37, 49]}, {"entity": "industrial robot", "entity_type": "product", "pos": [80, 96]}], "task": "NER"}
{"text": "Pandora ( also known as Pandora Media or Pandora Radio ) is an American music streaming and automated Recommender system internet radio service powered by the Music Genome Project and headquartered in Oakland , California .", "entity": [{"entity": "Pandora", "entity_type": "product", "pos": [0, 7]}, {"entity": "Pandora Media", "entity_type": "product", "pos": [24, 37]}, {"entity": "Pandora Radio", "entity_type": "product", "pos": [41, 54]}, {"entity": "American", "entity_type": "else", "pos": [63, 71]}, {"entity": "automated Recommender system", "entity_type": "product", "pos": [92, 120]}, {"entity": "Music Genome Project", "entity_type": "else", "pos": [159, 179]}, {"entity": "Oakland", "entity_type": "location", "pos": [201, 208]}, {"entity": "California", "entity_type": "location", "pos": [211, 221]}], "task": "NER"}
{"text": "She is a board member of the International Machine Learning Society , has been a member of AAAI Executive council , was PC co-chair of ICML 2011 , and has served as senior PC member for conferences including AAAI , ICML , IJCAI , ISWC , KDD , SIGMOD , UAI , VLDB , WSDM and WWW .", "entity": [{"entity": "International Machine Learning Society", "entity_type": "organization", "pos": [29, 67]}, {"entity": "AAAI Executive council", "entity_type": "organization", "pos": [91, 113]}, {"entity": "ICML 2011", "entity_type": "conference", "pos": [135, 144]}, {"entity": "AAAI", "entity_type": "conference", "pos": [208, 212]}, {"entity": "ICML", "entity_type": "conference", "pos": [215, 219]}, {"entity": "IJCAI", "entity_type": "conference", "pos": [222, 227]}, {"entity": "ISWC", "entity_type": "conference", "pos": [230, 234]}, {"entity": "KDD", "entity_type": "conference", "pos": [237, 240]}, {"entity": "SIGMOD", "entity_type": "conference", "pos": [243, 249]}, {"entity": "UAI", "entity_type": "conference", "pos": [252, 255]}, {"entity": "VLDB", "entity_type": "conference", "pos": [258, 262]}, {"entity": "WSDM", "entity_type": "conference", "pos": [265, 269]}, {"entity": "WWW", "entity_type": "conference", "pos": [274, 277]}], "task": "NER"}
{"text": "James S. Albus of the National Institute of Standards and Technology ( NIST ) developed the Robocrane , where the platform hangs from six cables instead of being supported by six jacks .", "entity": [{"entity": "James S. Albus", "entity_type": "researcher", "pos": [0, 14]}, {"entity": "National Institute of Standards and Technology", "entity_type": "organization", "pos": [22, 68]}, {"entity": "NIST", "entity_type": "organization", "pos": [71, 75]}, {"entity": "Robocrane", "entity_type": "product", "pos": [92, 101]}], "task": "NER"}
{"text": "Another class of direct search algorithms are the various evolutionary algorithm s , e.g. genetic algorithm s .", "entity": [{"entity": "direct search algorithms", "entity_type": "else", "pos": [17, 41]}, {"entity": "evolutionary algorithm", "entity_type": "algorithm", "pos": [58, 80]}, {"entity": "genetic algorithm", "entity_type": "algorithm", "pos": [90, 107]}], "task": "NER"}
{"text": "KUKA is a German manufacturer of industrial robot s and solution s for factory automation .", "entity": [{"entity": "KUKA", "entity_type": "organization", "pos": [0, 4]}, {"entity": "German", "entity_type": "else", "pos": [10, 16]}, {"entity": "industrial robot", "entity_type": "product", "pos": [33, 49]}], "task": "NER"}
{"text": "Other films between 2016 to 2020 that captured with IMAX camera 's were Zack Snyder ' s Batman v Superman : Dawn of Justice , Clint Eastwood ' s Sully , Damien Chazelle ' s First Man , Patty Jenkins ' Wonder Woman 1984 , Cary Joji Fukunaga ' s No Time to Die and Joseph Kosinski ' s Top Gun : Maverick .", "entity": [{"entity": "IMAX", "entity_type": "else", "pos": [52, 56]}, {"entity": "Zack Snyder", "entity_type": "person", "pos": [72, 83]}, {"entity": "Batman v Superman : Dawn of Justice", "entity_type": "else", "pos": [88, 123]}, {"entity": "Clint Eastwood", "entity_type": "person", "pos": [126, 140]}, {"entity": "Sully", "entity_type": "else", "pos": [145, 150]}, {"entity": "Damien Chazelle", "entity_type": "person", "pos": [153, 168]}, {"entity": "First Man", "entity_type": "else", "pos": [173, 182]}, {"entity": "Patty Jenkins", "entity_type": "person", "pos": [185, 198]}, {"entity": "Wonder Woman 1984", "entity_type": "else", "pos": [201, 218]}, {"entity": "Cary Joji Fukunaga", "entity_type": "person", "pos": [221, 239]}, {"entity": "No Time to Die", "entity_type": "else", "pos": [244, 258]}, {"entity": "Joseph Kosinski", "entity_type": "person", "pos": [263, 278]}, {"entity": "Top Gun : Maverick", "entity_type": "else", "pos": [283, 301]}], "task": "NER"}
{"text": "The trial of MICR E13B font was shown to the American Bankers Association ( ABA ) in July 1956 , which adopted it in 1958 as the MICR standard for negotiable document s in the United States .", "entity": [{"entity": "MICR E13B", "entity_type": "else", "pos": [13, 22]}, {"entity": "American Bankers Association", "entity_type": "organization", "pos": [45, 73]}, {"entity": "ABA", "entity_type": "organization", "pos": [76, 79]}, {"entity": "MICR", "entity_type": "else", "pos": [129, 133]}, {"entity": "United States", "entity_type": "country", "pos": [176, 189]}], "task": "NER"}
{"text": "Local search algorithms are widely applied to numerous hard computational problems , including problems from computer science ( particularly artificial intelligence ) , mathematics , operations research , engineering , and bioinformatics .", "entity": [{"entity": "Local search algorithms", "entity_type": "else", "pos": [0, 23]}, {"entity": "computer science", "entity_type": "field", "pos": [109, 125]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [141, 164]}, {"entity": "mathematics", "entity_type": "field", "pos": [169, 180]}, {"entity": "operations research", "entity_type": "field", "pos": [183, 202]}, {"entity": "engineering", "entity_type": "field", "pos": [205, 216]}, {"entity": "bioinformatics", "entity_type": "field", "pos": [223, 237]}], "task": "NER"}
{"text": "Gerd Gigerenzer ( born September 3 , 1947 , Wallersdorf , Germany ) is a Germany psychologist who has studied the use of bounded rationality and heuristic s in decision making .", "entity": [{"entity": "Gerd Gigerenzer", "entity_type": "researcher", "pos": [0, 15]}, {"entity": "Wallersdorf", "entity_type": "location", "pos": [44, 55]}, {"entity": "Germany", "entity_type": "country", "pos": [58, 65]}, {"entity": "Germany", "entity_type": "country", "pos": [73, 80]}, {"entity": "bounded rationality", "entity_type": "algorithm", "pos": [121, 140]}, {"entity": "heuristic", "entity_type": "algorithm", "pos": [145, 154]}, {"entity": "decision making", "entity_type": "task", "pos": [160, 175]}], "task": "NER"}
{"text": "to minimize the Mean squared error .", "entity": [{"entity": "Mean squared error", "entity_type": "metrics", "pos": [16, 34]}], "task": "NER"}
{"text": "But even an official language with a regulating academy , such as Standard French with the Académie française , is classified as a natural language ( for example , in the field of natural language processing ) , as its prescriptive points do not make it either constructed enough to be classified as a constructed language or controlled enough to be classified as a controlled natural language .", "entity": [{"entity": "Standard French", "entity_type": "else", "pos": [66, 81]}, {"entity": "Académie française", "entity_type": "organization", "pos": [91, 109]}, {"entity": "natural language processing", "entity_type": "field", "pos": [180, 207]}, {"entity": "constructed language", "entity_type": "else", "pos": [302, 322]}, {"entity": "controlled natural language", "entity_type": "else", "pos": [366, 393]}], "task": "NER"}
{"text": "There are a number of other metrics , most simply the accuracy or Fraction Correct ( FC ) , which measures the fraction of all instances that are correctly categorized ; the complement is the Fraction Incorrect ( FiC ) .", "entity": [{"entity": "accuracy", "entity_type": "metrics", "pos": [54, 62]}, {"entity": "Fraction Correct", "entity_type": "metrics", "pos": [66, 82]}, {"entity": "FC", "entity_type": "metrics", "pos": [85, 87]}, {"entity": "Fraction Incorrect", "entity_type": "metrics", "pos": [192, 210]}, {"entity": "FiC", "entity_type": "metrics", "pos": [213, 216]}], "task": "NER"}
{"text": "Cardie became a Fellow of the Association for Computational Linguistics in 2016 .", "entity": [{"entity": "Cardie", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "Association for Computational Linguistics", "entity_type": "conference", "pos": [30, 71]}], "task": "NER"}
{"text": "Learning the parameters math \\ theta / math is usually done by maximum likelihood learning for mathp ( Y _ i | X _ i ; \\ theta ) / math .", "entity": [{"entity": "maximum likelihood learning", "entity_type": "algorithm", "pos": [63, 90]}], "task": "NER"}
{"text": "Cluster analysis , and Non-negative matrix factorization for descriptive mining .", "entity": [{"entity": "Cluster analysis", "entity_type": "task", "pos": [0, 16]}, {"entity": "Non-negative matrix factorization", "entity_type": "algorithm", "pos": [23, 56]}, {"entity": "descriptive mining", "entity_type": "task", "pos": [61, 79]}], "task": "NER"}
{"text": "In computer science and the information technology that it enables , it has been a long-term challenge to the ability in computers to do natural language processing and machine learning .", "entity": [{"entity": "computer science", "entity_type": "field", "pos": [3, 19]}, {"entity": "information technology", "entity_type": "field", "pos": [28, 50]}, {"entity": "natural language processing", "entity_type": "field", "pos": [137, 164]}, {"entity": "machine learning", "entity_type": "field", "pos": [169, 185]}], "task": "NER"}
{"text": "( Code for Gabor feature extraction from images in MATLAB can be found at", "entity": [{"entity": "Gabor feature extraction", "entity_type": "algorithm", "pos": [11, 35]}, {"entity": "MATLAB", "entity_type": "product", "pos": [51, 57]}], "task": "NER"}
{"text": "The NeuralExpert centers the design specifications around the type of problem the user would like the neural network to solve ( Classification , Prediction , Function approximation or Cluster analysis ) .", "entity": [{"entity": "NeuralExpert", "entity_type": "else", "pos": [4, 16]}, {"entity": "neural network", "entity_type": "algorithm", "pos": [102, 116]}, {"entity": "Classification", "entity_type": "task", "pos": [128, 142]}, {"entity": "Prediction", "entity_type": "task", "pos": [145, 155]}, {"entity": "Function approximation", "entity_type": "task", "pos": [158, 180]}, {"entity": "Cluster analysis", "entity_type": "task", "pos": [184, 200]}], "task": "NER"}
{"text": "When the quantization step size ( Δ ) is small relative to the variation in the signal being quantized , it is relatively simple to show that the mean squared error produced by such a rounding operation will be approximately math \\ Delta ^ 2 / 12 / math.math", "entity": [{"entity": "quantization step size", "entity_type": "else", "pos": [9, 31]}, {"entity": "mean squared error", "entity_type": "metrics", "pos": [146, 164]}], "task": "NER"}
{"text": "The construction of a rich lexicon with a suitable ontology requires significant effort , e.g. , Wordnet lexicon required many person-years of effort. G. A. Miller , R. Beckwith , C. D. Fellbaum , D. Gross , K. Miller .", "entity": [{"entity": "Wordnet", "entity_type": "product", "pos": [97, 104]}, {"entity": "G. A. Miller", "entity_type": "researcher", "pos": [151, 163]}, {"entity": "R. Beckwith", "entity_type": "researcher", "pos": [166, 177]}, {"entity": "C. D. Fellbaum", "entity_type": "researcher", "pos": [180, 194]}, {"entity": "D. Gross", "entity_type": "researcher", "pos": [197, 205]}, {"entity": "K. Miller", "entity_type": "researcher", "pos": [208, 217]}], "task": "NER"}
{"text": "Kawasaki 's portfolio also includes retractable roofs , floors and other giant structures , the Sapporo Dome ' retractable surface is one example .", "entity": [{"entity": "Kawasaki", "entity_type": "organization", "pos": [0, 8]}, {"entity": "Sapporo Dome", "entity_type": "location", "pos": [96, 108]}], "task": "NER"}
{"text": "Kappa statistics such as Fleiss ' kappa and Cohen 's kappa are methods for calculating inter-rater reliability based on different assumptions about the marginal or prior distributions , and are increasingly used as chance corrected alternatives to accuracy in other contexts .", "entity": [{"entity": "Kappa statistics", "entity_type": "metrics", "pos": [0, 16]}, {"entity": "Fleiss ' kappa", "entity_type": "metrics", "pos": [25, 39]}, {"entity": "Cohen 's kappa", "entity_type": "metrics", "pos": [44, 58]}, {"entity": "inter-rater reliability", "entity_type": "metrics", "pos": [87, 110]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [248, 256]}], "task": "NER"}
{"text": "With his students Sepp Hochreiter , Felix Gers , Fred Cummins , Alex Graves , and others , Schmidhuber published increasingly sophisticated versions of a type of recurrent neural network called the long short-term memory ( LSTM ) .", "entity": [{"entity": "Sepp Hochreiter", "entity_type": "researcher", "pos": [18, 33]}, {"entity": "Felix Gers", "entity_type": "researcher", "pos": [36, 46]}, {"entity": "Fred Cummins", "entity_type": "researcher", "pos": [49, 61]}, {"entity": "Alex Graves", "entity_type": "researcher", "pos": [64, 75]}, {"entity": "Schmidhuber", "entity_type": "researcher", "pos": [91, 102]}, {"entity": "recurrent neural network", "entity_type": "algorithm", "pos": [162, 186]}, {"entity": "long short-term memory", "entity_type": "algorithm", "pos": [198, 220]}, {"entity": "LSTM", "entity_type": "algorithm", "pos": [223, 227]}], "task": "NER"}
{"text": "2004 - The first Cobot KUKA LBR 3 is released .", "entity": [{"entity": "Cobot KUKA LBR 3", "entity_type": "product", "pos": [17, 33]}], "task": "NER"}
{"text": "Two shallow approaches used to train and then disambiguate are Naive Bayes classifier and decision trees .", "entity": [{"entity": "Naive Bayes classifier", "entity_type": "algorithm", "pos": [63, 85]}, {"entity": "decision trees", "entity_type": "algorithm", "pos": [90, 104]}], "task": "NER"}
{"text": "The first practical forms of photography were introduced in January 1839 by Louis Daguerre and Henry Fox Talbot .", "entity": [{"entity": "photography", "entity_type": "else", "pos": [29, 40]}, {"entity": "Louis Daguerre", "entity_type": "person", "pos": [76, 90]}, {"entity": "Henry Fox Talbot", "entity_type": "person", "pos": [95, 111]}], "task": "NER"}
{"text": "For example , speech synthesis , combined with speech recognition , allows for interaction with mobile devices via language processing interfaces .", "entity": [{"entity": "speech synthesis", "entity_type": "task", "pos": [14, 30]}, {"entity": "speech recognition", "entity_type": "task", "pos": [47, 65]}, {"entity": "language processing", "entity_type": "field", "pos": [115, 134]}], "task": "NER"}
{"text": "Phidgets can be programmed using a variety of software and programming languages , ranging from Java to Microsoft Excel .", "entity": [{"entity": "Phidgets", "entity_type": "product", "pos": [0, 8]}, {"entity": "Java", "entity_type": "program language", "pos": [96, 100]}, {"entity": "Microsoft Excel", "entity_type": "product", "pos": [104, 119]}], "task": "NER"}
{"text": "The term machine learning was coined in 1959 by Arthur Samuel , an American IBMer and pioneer in the field of computer gaming and artificial intelligence .", "entity": [{"entity": "machine learning", "entity_type": "field", "pos": [9, 25]}, {"entity": "Arthur Samuel", "entity_type": "researcher", "pos": [48, 61]}, {"entity": "American IBMer", "entity_type": "else", "pos": [67, 81]}, {"entity": "computer gaming", "entity_type": "field", "pos": [110, 125]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [130, 153]}], "task": "NER"}
{"text": "The Israeli poet David Avidan , who was fascinated with future technologies and their relation to art , desired to explore the use of computers for writing literature .", "entity": [{"entity": "Israeli", "entity_type": "else", "pos": [4, 11]}, {"entity": "David Avidan", "entity_type": "person", "pos": [17, 29]}], "task": "NER"}
{"text": "As part of the GATEway Project in 2017 , Oxbotica trialled seven autonomous shuttle buses in Greenwich , navigating a two-mile riverside path near London 's The O2 Arena on a route also used by pedestrians and cyclists .", "entity": [{"entity": "GATEway Project", "entity_type": "else", "pos": [15, 30]}, {"entity": "Oxbotica", "entity_type": "organization", "pos": [41, 49]}, {"entity": "Greenwich", "entity_type": "location", "pos": [93, 102]}, {"entity": "London", "entity_type": "location", "pos": [147, 153]}, {"entity": "The O2 Arena", "entity_type": "location", "pos": [157, 169]}], "task": "NER"}
{"text": "An unrelated but commonly used combination of basic statistics from information retrieval is the F-score , being a ( possibly weighted ) harmonic mean of recall and precision where recall = sensitivity = TRUE positive rate , but specificity and precision are totally different measures .", "entity": [{"entity": "information retrieval", "entity_type": "task", "pos": [68, 89]}, {"entity": "F-score", "entity_type": "metrics", "pos": [97, 104]}, {"entity": "harmonic mean", "entity_type": "else", "pos": [137, 150]}, {"entity": "recall", "entity_type": "metrics", "pos": [154, 160]}, {"entity": "precision", "entity_type": "metrics", "pos": [165, 174]}, {"entity": "recall", "entity_type": "metrics", "pos": [181, 187]}, {"entity": "sensitivity", "entity_type": "metrics", "pos": [190, 201]}, {"entity": "TRUE positive rate", "entity_type": "metrics", "pos": [204, 222]}, {"entity": "specificity", "entity_type": "metrics", "pos": [229, 240]}, {"entity": "precision", "entity_type": "metrics", "pos": [245, 254]}], "task": "NER"}
{"text": "Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology , physics , mathematics , computer science , and electronic engineering to design artificial neural systems , such as vision systems , head-eye systems , auditory processors , and autonomous robots , whose physical architecture and design principles are based on those of biological nervous systems .", "entity": [{"entity": "Neuromorphic engineering", "entity_type": "field", "pos": [0, 24]}, {"entity": "biology", "entity_type": "field", "pos": [85, 92]}, {"entity": "physics", "entity_type": "field", "pos": [95, 102]}, {"entity": "mathematics", "entity_type": "field", "pos": [105, 116]}, {"entity": "computer science", "entity_type": "field", "pos": [119, 135]}, {"entity": "electronic engineering", "entity_type": "field", "pos": [142, 164]}, {"entity": "vision systems", "entity_type": "product", "pos": [211, 225]}, {"entity": "head-eye systems", "entity_type": "product", "pos": [228, 244]}, {"entity": "auditory processors", "entity_type": "product", "pos": [247, 266]}, {"entity": "autonomous robots", "entity_type": "product", "pos": [273, 290]}, {"entity": "biological nervous systems", "entity_type": "product", "pos": [365, 391]}], "task": "NER"}
{"text": "To be specific , the BIBO stability criterion requires that the ROC of the system includes the unit circle .", "entity": [{"entity": "BIBO stability criterion", "entity_type": "metrics", "pos": [21, 45]}, {"entity": "ROC", "entity_type": "metrics", "pos": [64, 67]}], "task": "NER"}
{"text": "2 The program was rewritten in Java beginning in 1998 .", "entity": [{"entity": "Java", "entity_type": "program language", "pos": [31, 35]}], "task": "NER"}
{"text": "The MCC can be calculated directly from the confusion matrix using the formula :", "entity": [{"entity": "MCC", "entity_type": "metrics", "pos": [4, 7]}, {"entity": "confusion matrix", "entity_type": "metrics", "pos": [44, 60]}], "task": "NER"}
{"text": "It was developed by a team at the MIT-IBM Watson AI Lab and first presented at the 2018 International Conference on Learning Representations .", "entity": [{"entity": "MIT-IBM Watson AI Lab", "entity_type": "organization", "pos": [34, 55]}, {"entity": "2018 International Conference on Learning Representations", "entity_type": "conference", "pos": [83, 140]}], "task": "NER"}
{"text": "When the TRUE prevalence s for the two positive variables are equal as assumed in Fleiss kappa and F-score , that is the number of positive predictions matches the number of positive classes in the dichotomous ( two class ) case , the different kappa and correlation measure collapse to identity with Youden 's J , and recall , precision and F-score are similarly identical with accuracy .", "entity": [{"entity": "TRUE prevalence", "entity_type": "metrics", "pos": [9, 24]}, {"entity": "Fleiss kappa", "entity_type": "metrics", "pos": [82, 94]}, {"entity": "F-score", "entity_type": "metrics", "pos": [99, 106]}, {"entity": "kappa", "entity_type": "metrics", "pos": [245, 250]}, {"entity": "correlation", "entity_type": "metrics", "pos": [255, 266]}, {"entity": "Youden 's J", "entity_type": "researcher", "pos": [301, 312]}, {"entity": "recall", "entity_type": "metrics", "pos": [319, 325]}, {"entity": "precision", "entity_type": "metrics", "pos": [328, 337]}, {"entity": "F-score", "entity_type": "metrics", "pos": [342, 349]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [379, 387]}], "task": "NER"}
{"text": "The Building Educational Applications workshop ( BEA ) at NAACL 2013 hosted the inaugural NLI shared task. Tetreault et al , 2013 The competition resulted in 29 entries from teams across the globe , 24 of which also published a paper describing their systems and approaches .", "entity": [{"entity": "Building Educational Applications workshop", "entity_type": "conference", "pos": [4, 46]}, {"entity": "BEA", "entity_type": "conference", "pos": [49, 52]}, {"entity": "NAACL", "entity_type": "conference", "pos": [58, 63]}, {"entity": "NLI shared task.", "entity_type": "task", "pos": [90, 106]}, {"entity": "Tetreault", "entity_type": "researcher", "pos": [107, 116]}], "task": "NER"}
{"text": "The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states called the Viterbi path that results in a sequence of observed events , especially in the context of Markov information source s and hidden Markov model s ( HMM ) .", "entity": [{"entity": "Viterbi algorithm", "entity_type": "algorithm", "pos": [4, 21]}, {"entity": "dynamic programming algorithm", "entity_type": "algorithm", "pos": [27, 56]}, {"entity": "hidden states", "entity_type": "else", "pos": [97, 110]}, {"entity": "Viterbi path", "entity_type": "else", "pos": [122, 134]}, {"entity": "Markov information source", "entity_type": "else", "pos": [212, 237]}, {"entity": "hidden Markov model", "entity_type": "algorithm", "pos": [244, 263]}, {"entity": "HMM", "entity_type": "algorithm", "pos": [268, 271]}], "task": "NER"}
{"text": "In statistics , multinomial logistic regression is a classification method that generalizes logistic regression to multiclass classification , i.e. with more than two possible discrete outcomes .", "entity": [{"entity": "statistics", "entity_type": "field", "pos": [3, 13]}, {"entity": "multinomial logistic regression", "entity_type": "algorithm", "pos": [16, 47]}, {"entity": "classification method", "entity_type": "else", "pos": [53, 74]}, {"entity": "logistic regression", "entity_type": "algorithm", "pos": [92, 111]}, {"entity": "multiclass classification", "entity_type": "task", "pos": [115, 140]}], "task": "NER"}
{"text": "Hidden Markov models are known for their applications to reinforcement learning and temporal pattern recognition such as speech , handwriting recognition , gesture recognition , Thad Starner , Alex Pentland .", "entity": [{"entity": "Hidden Markov models", "entity_type": "algorithm", "pos": [0, 20]}, {"entity": "reinforcement learning", "entity_type": "field", "pos": [57, 79]}, {"entity": "temporal pattern recognition", "entity_type": "field", "pos": [84, 112]}, {"entity": "speech", "entity_type": "task", "pos": [121, 127]}, {"entity": "handwriting recognition", "entity_type": "task", "pos": [130, 153]}, {"entity": "gesture recognition", "entity_type": "task", "pos": [156, 175]}, {"entity": "Thad Starner", "entity_type": "researcher", "pos": [178, 190]}, {"entity": "Alex Pentland", "entity_type": "researcher", "pos": [193, 206]}], "task": "NER"}
{"text": "Essentially , this means that if the n-gram has been seen more than k times in training , the conditional probability of a word given its history is proportional to the maximum likelihood estimate of that n -gram .", "entity": [{"entity": "n-gram", "entity_type": "else", "pos": [37, 43]}, {"entity": "maximum likelihood estimate", "entity_type": "metrics", "pos": [169, 196]}, {"entity": "n -gram", "entity_type": "else", "pos": [205, 212]}], "task": "NER"}
{"text": "He is interested in knowledge representation , commonsense reasoning , and natural language understanding , believing that deep language understanding can only currently be achieved by significant hand-engineering of semantically-rich formalisms coupled with statistical preferences .", "entity": [{"entity": "knowledge representation", "entity_type": "task", "pos": [20, 44]}, {"entity": "commonsense reasoning", "entity_type": "task", "pos": [47, 68]}, {"entity": "natural language understanding", "entity_type": "task", "pos": [75, 105]}, {"entity": "deep language understanding", "entity_type": "task", "pos": [123, 150]}, {"entity": "hand-engineering", "entity_type": "else", "pos": [197, 213]}], "task": "NER"}
{"text": "In JavaScript , Python or", "entity": [{"entity": "JavaScript", "entity_type": "program language", "pos": [3, 13]}, {"entity": "Python", "entity_type": "program language", "pos": [16, 22]}], "task": "NER"}
{"text": "The Newcomb Awards are announced in the AI Magazine published by AAAI .", "entity": [{"entity": "Newcomb Awards", "entity_type": "else", "pos": [4, 18]}, {"entity": "AI Magazine", "entity_type": "else", "pos": [40, 51]}, {"entity": "AAAI", "entity_type": "conference", "pos": [65, 69]}], "task": "NER"}
{"text": "The Mean squared error on a test set of 100 exemplars is 0.084 , smaller than the unnormalized error .", "entity": [{"entity": "Mean squared error", "entity_type": "metrics", "pos": [4, 22]}], "task": "NER"}
{"text": "The F-score has been widely used in the natural language processing literature , such as the evaluation of named entity recognition ( NER ) and word segmentation .", "entity": [{"entity": "F-score", "entity_type": "metrics", "pos": [4, 11]}, {"entity": "natural language processing", "entity_type": "field", "pos": [40, 67]}, {"entity": "named entity recognition", "entity_type": "task", "pos": [107, 131]}, {"entity": "NER", "entity_type": "task", "pos": [134, 137]}, {"entity": "word segmentation", "entity_type": "task", "pos": [144, 161]}], "task": "NER"}
{"text": "Chatbots are typically used in dialog systems for various purposes including customer service , request routing , or for information gathering .", "entity": [{"entity": "Chatbots", "entity_type": "product", "pos": [0, 8]}, {"entity": "dialog systems", "entity_type": "product", "pos": [31, 45]}, {"entity": "request routing", "entity_type": "else", "pos": [96, 111]}, {"entity": "information gathering", "entity_type": "else", "pos": [121, 142]}], "task": "NER"}
{"text": "Important journals include the IEEE Transactions on Speech and Audio Processing ( later renamed IEEE Transactions on Audio , Speech and Language Processing and since Sept 2014 renamed IEEE / ACM Transactions on Audio , Speech and Language Processing - after merging with an ACM publication ) , Computer Speech and Language , and Speech Communication .", "entity": [{"entity": "IEEE Transactions on Speech and Audio Processing", "entity_type": "conference", "pos": [31, 79]}, {"entity": "IEEE Transactions on Audio , Speech and Language Processing", "entity_type": "conference", "pos": [96, 155]}, {"entity": "IEEE / ACM Transactions on Audio , Speech and Language Processing", "entity_type": "conference", "pos": [184, 249]}, {"entity": "ACM", "entity_type": "conference", "pos": [274, 277]}, {"entity": "Computer Speech and Language", "entity_type": "conference", "pos": [294, 322]}, {"entity": "Speech Communication", "entity_type": "conference", "pos": [329, 349]}], "task": "NER"}
{"text": "EM is frequently used for data clustering in machine learning and computer vision .", "entity": [{"entity": "EM", "entity_type": "algorithm", "pos": [0, 2]}, {"entity": "data clustering", "entity_type": "task", "pos": [26, 41]}, {"entity": "machine learning", "entity_type": "field", "pos": [45, 61]}, {"entity": "computer vision", "entity_type": "field", "pos": [66, 81]}], "task": "NER"}
{"text": "While there is no perfect way of describing the confusion matrix of TRUE and FALSE positives and negatives by a single number , the Matthews correlation coefficient is generally regarded as being one of the best such measures .", "entity": [{"entity": "confusion matrix", "entity_type": "metrics", "pos": [48, 64]}, {"entity": "Matthews correlation coefficient", "entity_type": "metrics", "pos": [132, 164]}], "task": "NER"}
{"text": "As data set s have grown in size and complexity , direct hands-on data analysis has been augmented with indirect , automated data processing , aided by other discoveries in computer science , specially in the field of machine learning , such as neural networks , cluster analysis , genetic algorithms ( 1950s ) , decision tree learning and decision rules ( 1960s ) , and support vector machines ( 1990s ) .", "entity": [{"entity": "data analysis", "entity_type": "field", "pos": [66, 79]}, {"entity": "computer science", "entity_type": "field", "pos": [173, 189]}, {"entity": "machine learning", "entity_type": "field", "pos": [218, 234]}, {"entity": "neural networks", "entity_type": "algorithm", "pos": [245, 260]}, {"entity": "cluster analysis", "entity_type": "task", "pos": [263, 279]}, {"entity": "genetic algorithms", "entity_type": "algorithm", "pos": [282, 300]}, {"entity": "decision tree learning", "entity_type": "algorithm", "pos": [313, 335]}, {"entity": "decision rules", "entity_type": "algorithm", "pos": [340, 354]}, {"entity": "support vector machines", "entity_type": "algorithm", "pos": [371, 394]}], "task": "NER"}
{"text": "In the fall of 2005 , Thrun published a textbook entitled Probabilistic Robotics together with his long-term co-workers Dieter Fox and Wolfram Burgard .", "entity": [{"entity": "Thrun", "entity_type": "researcher", "pos": [22, 27]}, {"entity": "Probabilistic Robotics", "entity_type": "else", "pos": [58, 80]}, {"entity": "Dieter Fox", "entity_type": "researcher", "pos": [120, 130]}, {"entity": "Wolfram Burgard", "entity_type": "researcher", "pos": [135, 150]}], "task": "NER"}
{"text": "John D. Lafferty , Andrew McCallum and Pereiramath as follows :", "entity": [{"entity": "John D. Lafferty", "entity_type": "researcher", "pos": [0, 16]}, {"entity": "Andrew McCallum", "entity_type": "researcher", "pos": [19, 34]}, {"entity": "Pereiramath", "entity_type": "researcher", "pos": [39, 50]}], "task": "NER"}
{"text": "Question answering ( QA ) is a computer science discipline within the fields of information retrieval and natural language processing ( NLP ) , which is concerned with building systems that automatically answer questions posed by humans in a natural language .", "entity": [{"entity": "Question answering", "entity_type": "task", "pos": [0, 18]}, {"entity": "QA", "entity_type": "task", "pos": [21, 23]}, {"entity": "computer science", "entity_type": "field", "pos": [31, 47]}, {"entity": "information retrieval", "entity_type": "field", "pos": [80, 101]}, {"entity": "natural language processing", "entity_type": "field", "pos": [106, 133]}, {"entity": "NLP", "entity_type": "field", "pos": [136, 139]}], "task": "NER"}
{"text": "However , in the version of the metric used by NIST evaluations prior to 2009 , the shortest reference sentence had been used instead .", "entity": [{"entity": "NIST", "entity_type": "metrics", "pos": [47, 51]}], "task": "NER"}
{"text": "On August 27 , 2018 , Toyota announced an investment of $ 500 Million in Uber ' s autonomous car s .", "entity": [{"entity": "Toyota", "entity_type": "person", "pos": [22, 28]}, {"entity": "Uber", "entity_type": "organization", "pos": [73, 77]}, {"entity": "autonomous car", "entity_type": "product", "pos": [82, 96]}], "task": "NER"}
{"text": "The sample maximum is the maximum likelihood estimator for the population maximum , but , as discussed above , it is biased .", "entity": [{"entity": "maximum likelihood estimator", "entity_type": "metrics", "pos": [26, 54]}], "task": "NER"}
{"text": "LSI helps overcome synonymy by increasing recall , one of the most problematic constraints of Boolean keyword queries and vector space models .", "entity": [{"entity": "LSI", "entity_type": "task", "pos": [0, 3]}, {"entity": "synonymy", "entity_type": "else", "pos": [19, 27]}, {"entity": "recall", "entity_type": "metrics", "pos": [42, 48]}, {"entity": "Boolean keyword queries", "entity_type": "algorithm", "pos": [94, 117]}, {"entity": "vector space models", "entity_type": "algorithm", "pos": [122, 141]}], "task": "NER"}
{"text": "Data acquisition applications are usually controlled by software programs developed using various general purpose programming languages such as Assembly , BASIC , C , C + + , C # , Fortran , Java , LabVIEW , Lisp , Pascal , etc .", "entity": [{"entity": "Data acquisition", "entity_type": "task", "pos": [0, 16]}, {"entity": "Assembly", "entity_type": "program language", "pos": [144, 152]}, {"entity": "BASIC", "entity_type": "program language", "pos": [155, 160]}, {"entity": "C", "entity_type": "program language", "pos": [163, 164]}, {"entity": "C + +", "entity_type": "program language", "pos": [167, 172]}, {"entity": "C #", "entity_type": "program language", "pos": [175, 178]}, {"entity": "Fortran", "entity_type": "program language", "pos": [181, 188]}, {"entity": "Java", "entity_type": "program language", "pos": [191, 195]}, {"entity": "LabVIEW", "entity_type": "program language", "pos": [198, 205]}, {"entity": "Lisp", "entity_type": "program language", "pos": [208, 212]}, {"entity": "Pascal", "entity_type": "program language", "pos": [215, 221]}], "task": "NER"}
{"text": "In 2003 , Honda released its Cog advertisement in the UK and on the Internet .", "entity": [{"entity": "Honda", "entity_type": "organization", "pos": [10, 15]}, {"entity": "Cog", "entity_type": "product", "pos": [29, 32]}, {"entity": "UK", "entity_type": "country", "pos": [54, 56]}], "task": "NER"}
{"text": "The Association for Computational Linguistics defines computational linguistics as :", "entity": [{"entity": "Association for Computational Linguistics", "entity_type": "conference", "pos": [4, 45]}, {"entity": "computational linguistics", "entity_type": "field", "pos": [54, 79]}], "task": "NER"}
{"text": "Expectation-maximization algorithm s may be employed to calculate approximate maximum likelihood estimates of unknown state-space parameters within minimum-variance filters and smoothers .", "entity": [{"entity": "Expectation-maximization algorithm", "entity_type": "algorithm", "pos": [0, 34]}, {"entity": "maximum likelihood estimates", "entity_type": "algorithm", "pos": [78, 106]}], "task": "NER"}
{"text": "Correspondents included former Baywatch actresses Donna D 'Errico , Carmen Electra , and Traci Bingham , former Playboy Playmate Heidi Mark , comedian Arj Barker and identical twins Randy and Jason Sklar .", "entity": [{"entity": "Baywatch", "entity_type": "else", "pos": [31, 39]}, {"entity": "Donna D 'Errico", "entity_type": "person", "pos": [50, 65]}, {"entity": "Carmen Electra", "entity_type": "person", "pos": [68, 82]}, {"entity": "Traci Bingham", "entity_type": "person", "pos": [89, 102]}, {"entity": "Playboy Playmate", "entity_type": "else", "pos": [112, 128]}, {"entity": "Heidi Mark", "entity_type": "person", "pos": [129, 139]}, {"entity": "Arj Barker", "entity_type": "person", "pos": [151, 161]}, {"entity": "Randy", "entity_type": "person", "pos": [182, 187]}, {"entity": "Jason Sklar", "entity_type": "person", "pos": [192, 203]}], "task": "NER"}
{"text": "It is commonly used to generate representations for speech recognition ( ASR ) , e.g. the CMU Sphinx system , and speech synthesis ( TTS ) , e.g. the Festival system .", "entity": [{"entity": "speech recognition", "entity_type": "task", "pos": [52, 70]}, {"entity": "ASR", "entity_type": "task", "pos": [73, 76]}, {"entity": "CMU Sphinx system", "entity_type": "product", "pos": [90, 107]}, {"entity": "speech synthesis", "entity_type": "task", "pos": [114, 130]}, {"entity": "TTS", "entity_type": "task", "pos": [133, 136]}, {"entity": "Festival system", "entity_type": "product", "pos": [150, 165]}], "task": "NER"}
{"text": "Sensitivity or TRUE Positive Rate ( TPR ) , also known as recall , is the proportion of people that tested positive and are positive ( TRUE Positive , TP ) of all the people that actually are positive ( Condition Positive , CP = TP + FN ) .", "entity": [{"entity": "Sensitivity", "entity_type": "metrics", "pos": [0, 11]}, {"entity": "TRUE Positive Rate", "entity_type": "metrics", "pos": [15, 33]}, {"entity": "TPR", "entity_type": "metrics", "pos": [36, 39]}, {"entity": "recall", "entity_type": "metrics", "pos": [58, 64]}, {"entity": "TRUE Positive", "entity_type": "metrics", "pos": [135, 148]}, {"entity": "TP", "entity_type": "metrics", "pos": [151, 153]}, {"entity": "Condition Positive", "entity_type": "metrics", "pos": [203, 221]}, {"entity": "CP", "entity_type": "metrics", "pos": [224, 226]}, {"entity": "TP + FN", "entity_type": "metrics", "pos": [229, 236]}], "task": "NER"}
{"text": "Popular speech recognition conferences held each year or two include SpeechTEK and SpeechTEK Europe , ICASSP , Interspeech / Eurospeech , and the IEEE ASRU .", "entity": [{"entity": "speech recognition", "entity_type": "task", "pos": [8, 26]}, {"entity": "SpeechTEK", "entity_type": "conference", "pos": [69, 78]}, {"entity": "SpeechTEK Europe", "entity_type": "conference", "pos": [83, 99]}, {"entity": "ICASSP", "entity_type": "conference", "pos": [102, 108]}, {"entity": "Interspeech", "entity_type": "conference", "pos": [111, 122]}, {"entity": "Eurospeech", "entity_type": "conference", "pos": [125, 135]}, {"entity": "IEEE ASRU", "entity_type": "conference", "pos": [146, 155]}], "task": "NER"}
{"text": "Devol collaborated with Engelberger , who served as president of the company , to engineer and produce an industrial robot under the brand name Unimate .", "entity": [{"entity": "Devol", "entity_type": "researcher", "pos": [0, 5]}, {"entity": "Engelberger", "entity_type": "researcher", "pos": [24, 35]}, {"entity": "industrial robot", "entity_type": "product", "pos": [106, 122]}, {"entity": "Unimate", "entity_type": "product", "pos": [144, 151]}], "task": "NER"}
{"text": "A Hidden Markov model ( HMM ) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved ( hidden ) states .", "entity": [{"entity": "Hidden Markov model", "entity_type": "algorithm", "pos": [2, 21]}, {"entity": "HMM", "entity_type": "algorithm", "pos": [24, 27]}, {"entity": "statistical Markov model", "entity_type": "algorithm", "pos": [35, 59]}, {"entity": "Markov process", "entity_type": "algorithm", "pos": [113, 127]}], "task": "NER"}
{"text": "This property , undesirable in many applications , has led researchers to use alternatives such as the mean absolute error , or those based on the median .", "entity": [{"entity": "mean absolute error", "entity_type": "metrics", "pos": [103, 122]}, {"entity": "median", "entity_type": "else", "pos": [147, 153]}], "task": "NER"}
{"text": "Such a sequence ( which depends on the outcome of the investigation of previous attributes at each stage ) is called a decision tree and applied in the area of machine learning known as decision tree learning .", "entity": [{"entity": "decision tree", "entity_type": "algorithm", "pos": [119, 132]}, {"entity": "machine learning", "entity_type": "field", "pos": [160, 176]}, {"entity": "decision tree learning", "entity_type": "algorithm", "pos": [186, 208]}], "task": "NER"}
{"text": "As in factor analysis , the LCA can also be used to classify case according to their maximum likelihood class membership .", "entity": [{"entity": "factor analysis", "entity_type": "task", "pos": [6, 21]}, {"entity": "LCA", "entity_type": "algorithm", "pos": [28, 31]}, {"entity": "maximum likelihood", "entity_type": "algorithm", "pos": [85, 103]}], "task": "NER"}
{"text": "Supervised neural networks that use a mean squared error ( MSE ) cost function can use formal statistical methods to determine the confidence of the trained model .", "entity": [{"entity": "Supervised neural networks", "entity_type": "algorithm", "pos": [0, 26]}, {"entity": "mean squared error", "entity_type": "metrics", "pos": [38, 56]}, {"entity": "MSE", "entity_type": "metrics", "pos": [59, 62]}, {"entity": "cost function", "entity_type": "else", "pos": [65, 78]}], "task": "NER"}
{"text": "This can be directly expressed as a linear program , but it is also equivalent to Tikhonov regularization with the hinge loss function , mathV ( f ( x ) , y ) = \\ max ( 0 , 1 - yf ( x ) ) / math :", "entity": [{"entity": "Tikhonov regularization", "entity_type": "algorithm", "pos": [82, 105]}, {"entity": "hinge loss function", "entity_type": "metrics", "pos": [115, 134]}], "task": "NER"}
{"text": "The following technique was described in Breiman 's original paper and is implemented in the R package randomForest .", "entity": [{"entity": "Breiman", "entity_type": "researcher", "pos": [41, 48]}, {"entity": "R package randomForest", "entity_type": "product", "pos": [93, 115]}], "task": "NER"}
{"text": "Traditional image quality measures , such as PSNR , are typically performed on fixed resolution images and do not take into account some aspects of the human visual system , like the change in spatial resolution across the retina .", "entity": [{"entity": "PSNR", "entity_type": "metrics", "pos": [45, 49]}, {"entity": "retina", "entity_type": "else", "pos": [223, 229]}], "task": "NER"}
{"text": "John Ireland , Joanne Dru and Macdonald Carey starred in the Jack Broder color production Hannah Lee , which premiered June 19 , 1953 .", "entity": [{"entity": "John Ireland", "entity_type": "person", "pos": [0, 12]}, {"entity": "Joanne Dru", "entity_type": "person", "pos": [15, 25]}, {"entity": "Macdonald Carey", "entity_type": "person", "pos": [30, 45]}, {"entity": "Jack Broder", "entity_type": "person", "pos": [61, 72]}, {"entity": "Hannah Lee", "entity_type": "else", "pos": [90, 100]}], "task": "NER"}
{"text": "That process is called image registration , and uses different methods of computer vision , mostly related to tracking .", "entity": [{"entity": "image registration", "entity_type": "task", "pos": [23, 41]}, {"entity": "computer vision", "entity_type": "field", "pos": [74, 89]}, {"entity": "tracking", "entity_type": "task", "pos": [110, 118]}], "task": "NER"}
{"text": "Now let us start explaining the different possible relations between predicted and actual outcome : Confusion matrix", "entity": [], "task": "NER"}
{"text": "The VOICEBOX speech processing toolbox for MATLAB implements the conversion and its inverse as :", "entity": [{"entity": "VOICEBOX", "entity_type": "product", "pos": [4, 12]}, {"entity": "speech processing toolbox", "entity_type": "else", "pos": [13, 38]}, {"entity": "MATLAB", "entity_type": "product", "pos": [43, 49]}], "task": "NER"}
{"text": "Prolog is a logic programming language associated with artificial intelligence and computational linguistics .", "entity": [{"entity": "Prolog", "entity_type": "program language", "pos": [0, 6]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [55, 78]}, {"entity": "computational linguistics", "entity_type": "field", "pos": [83, 108]}], "task": "NER"}
{"text": "Milner has received numerous awards for her contributions to neuroscience and psychology including memberships in the Royal Society of London , the Royal Society of Canada and the National Academy of Sciences .", "entity": [{"entity": "Milner", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "neuroscience", "entity_type": "field", "pos": [61, 73]}, {"entity": "psychology", "entity_type": "field", "pos": [78, 88]}, {"entity": "Royal Society of London", "entity_type": "organization", "pos": [118, 141]}, {"entity": "Royal Society of Canada", "entity_type": "organization", "pos": [148, 171]}, {"entity": "National Academy of Sciences", "entity_type": "organization", "pos": [180, 208]}], "task": "NER"}
{"text": "By combining these operators one can obtain algorithms for many image processing tasks , such as feature extraction , image segmentation , image sharpening , image filtering , and classification .", "entity": [{"entity": "image processing", "entity_type": "field", "pos": [64, 80]}, {"entity": "feature extraction", "entity_type": "task", "pos": [97, 115]}, {"entity": "image segmentation", "entity_type": "task", "pos": [118, 136]}, {"entity": "image sharpening", "entity_type": "task", "pos": [139, 155]}, {"entity": "image filtering", "entity_type": "task", "pos": [158, 173]}, {"entity": "classification", "entity_type": "task", "pos": [180, 194]}], "task": "NER"}
{"text": "As of 2017 , he is a professor at the Collège de France and , since 1989 , the director of INSERM Unit 562 , Cognitive Neuroimaging .", "entity": [{"entity": "Collège de France", "entity_type": "university", "pos": [38, 55]}, {"entity": "INSERM Unit 562", "entity_type": "organization", "pos": [91, 106]}, {"entity": "Cognitive Neuroimaging", "entity_type": "field", "pos": [109, 131]}], "task": "NER"}
{"text": "There are many approaches to learning these embeddings , notably using Bayesian clustering frameworks or energy-based frameworks , and more recently , TransE ( Conference on Neural Information Processing Systems 2013 ) .", "entity": [{"entity": "Bayesian clustering frameworks", "entity_type": "algorithm", "pos": [71, 101]}, {"entity": "energy-based frameworks", "entity_type": "algorithm", "pos": [105, 128]}, {"entity": "TransE", "entity_type": "conference", "pos": [151, 157]}, {"entity": "Conference on Neural Information Processing Systems 2013", "entity_type": "conference", "pos": [160, 216]}], "task": "NER"}
{"text": "It is an alternative to the Word error rate ( Word Error Rate ) used in several countries .", "entity": [{"entity": "Word error rate", "entity_type": "metrics", "pos": [28, 43]}, {"entity": "Word Error Rate", "entity_type": "metrics", "pos": [46, 61]}], "task": "NER"}
{"text": "ANNs have been used on a variety of tasks , including computer vision , speech recognition , machine translation , social network filtering , playing board and video games , medical diagnosis , and even in activities that have traditionally been considered as reserved to humans , like painting .", "entity": [{"entity": "ANNs", "entity_type": "algorithm", "pos": [0, 4]}, {"entity": "computer vision", "entity_type": "field", "pos": [54, 69]}, {"entity": "speech recognition", "entity_type": "task", "pos": [72, 90]}, {"entity": "machine translation", "entity_type": "task", "pos": [93, 112]}, {"entity": "social network filtering", "entity_type": "task", "pos": [115, 139]}, {"entity": "playing board and video games", "entity_type": "task", "pos": [142, 171]}, {"entity": "medical diagnosis", "entity_type": "task", "pos": [174, 191]}, {"entity": "painting", "entity_type": "task", "pos": [286, 294]}], "task": "NER"}
{"text": "Modular Audio Recognition Framework ( MARF ) is an open-source research platform and a collection of voice , sound , speech , text and natural language processing ( NLP ) algorithm s written in Java and arranged into a modular and extensible framework that attempts to facilitate addition of new algorithm s .", "entity": [{"entity": "Modular Audio Recognition Framework", "entity_type": "product", "pos": [0, 35]}, {"entity": "MARF", "entity_type": "product", "pos": [38, 42]}, {"entity": "natural language processing", "entity_type": "field", "pos": [135, 162]}, {"entity": "NLP", "entity_type": "field", "pos": [165, 168]}, {"entity": "Java", "entity_type": "program language", "pos": [194, 198]}], "task": "NER"}
{"text": "In 2018 , a report by the civil liberties and rights campaigning organisation Big Brother Watch revealed that two United Kingdom police forces , South Wales Police and the Metropolitan Police , were using live facial recognition at public events and in public spaces , in September 2019 , South Wales Police use of facial recognition was ruled lawful .", "entity": [{"entity": "Big Brother Watch", "entity_type": "organization", "pos": [78, 95]}, {"entity": "United Kingdom", "entity_type": "country", "pos": [114, 128]}, {"entity": "South Wales Police", "entity_type": "organization", "pos": [145, 163]}, {"entity": "Metropolitan Police", "entity_type": "organization", "pos": [172, 191]}, {"entity": "facial recognition", "entity_type": "task", "pos": [210, 228]}, {"entity": "South Wales Police", "entity_type": "organization", "pos": [289, 307]}, {"entity": "facial recognition", "entity_type": "task", "pos": [315, 333]}], "task": "NER"}
{"text": "ANIMAL has been ported to R , a freely available language and environment for statistical computing and graphics .", "entity": [{"entity": "ANIMAL", "entity_type": "product", "pos": [0, 6]}, {"entity": "R", "entity_type": "program language", "pos": [26, 27]}, {"entity": "statistical computing", "entity_type": "field", "pos": [78, 99]}, {"entity": "graphics", "entity_type": "field", "pos": [104, 112]}], "task": "NER"}
{"text": "Time-inhomogeneous hidden Bernoulli model ( TI-HBM ) is an alternative to hidden Markov model ( HMM ) for automatic speech recognition .", "entity": [{"entity": "Time-inhomogeneous hidden Bernoulli model", "entity_type": "algorithm", "pos": [0, 41]}, {"entity": "TI-HBM", "entity_type": "algorithm", "pos": [44, 50]}, {"entity": "hidden Markov model", "entity_type": "algorithm", "pos": [74, 93]}, {"entity": "HMM", "entity_type": "algorithm", "pos": [96, 99]}, {"entity": "automatic speech recognition", "entity_type": "task", "pos": [106, 134]}], "task": "NER"}
{"text": "In July 2016 , Nvidia demonstrated during SIGGRAPH a new method of foveated rendering claimed to be invisible to users .", "entity": [{"entity": "Nvidia", "entity_type": "organization", "pos": [15, 21]}, {"entity": "SIGGRAPH", "entity_type": "conference", "pos": [42, 50]}], "task": "NER"}
{"text": "Both rely on speech act theory developed by John Searle in the 1960s and enhanced by Terry Winograd and Flores in the 1970s .", "entity": [{"entity": "speech act theory", "entity_type": "else", "pos": [13, 30]}, {"entity": "John Searle", "entity_type": "researcher", "pos": [44, 55]}, {"entity": "Terry Winograd", "entity_type": "researcher", "pos": [85, 99]}, {"entity": "Flores", "entity_type": "researcher", "pos": [104, 110]}], "task": "NER"}
{"text": "Neural network models of concept formation and the structure of knowledge have opened powerful hierarchical models of knowledge organization such as George Miller ' s Wordnet .", "entity": [{"entity": "Neural network models", "entity_type": "algorithm", "pos": [0, 21]}, {"entity": "George Miller", "entity_type": "researcher", "pos": [149, 162]}, {"entity": "Wordnet", "entity_type": "product", "pos": [167, 174]}], "task": "NER"}
{"text": "Template matching has various applications and is used in such fields as face recognition ( see facial recognition system ) and medical image processing .", "entity": [{"entity": "Template matching", "entity_type": "algorithm", "pos": [0, 17]}, {"entity": "face recognition", "entity_type": "task", "pos": [73, 89]}, {"entity": "facial recognition system", "entity_type": "product", "pos": [96, 121]}, {"entity": "medical image processing", "entity_type": "task", "pos": [128, 152]}], "task": "NER"}
{"text": "However , usage only became widespread in 2005 when Navneet Dalal and Bill Triggs , researchers for the French National Institute for Research in Computer Science and Automation ( INRIA ) , presented their supplementary work on HOG descriptors at the Conference on Computer Vision and Pattern Recognition ( CVPR ) .", "entity": [{"entity": "Navneet Dalal", "entity_type": "researcher", "pos": [52, 65]}, {"entity": "Bill Triggs", "entity_type": "researcher", "pos": [70, 81]}, {"entity": "French National Institute for Research in Computer Science and Automation", "entity_type": "organization", "pos": [104, 177]}, {"entity": "INRIA", "entity_type": "organization", "pos": [180, 185]}, {"entity": "HOG descriptors", "entity_type": "algorithm", "pos": [228, 243]}, {"entity": "Conference on Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [251, 304]}, {"entity": "CVPR", "entity_type": "conference", "pos": [307, 311]}], "task": "NER"}
{"text": "Prior to joining the Penn faculty in 2002 , he spent a decade ( 1991-2001 ) in AT & T Labs and Bell Labs , including as head of the AI department with colleagues including Michael L. Littman , David A. McAllester , and Richard S. Sutton ; Secure Systems Research department ; and Machine Learning department with members such as Michael Collins and the leader ) .", "entity": [{"entity": "Penn", "entity_type": "university", "pos": [21, 25]}, {"entity": "AT & T Labs", "entity_type": "organization", "pos": [79, 90]}, {"entity": "Bell Labs", "entity_type": "organization", "pos": [95, 104]}, {"entity": "AI", "entity_type": "field", "pos": [132, 134]}, {"entity": "Michael L. Littman", "entity_type": "researcher", "pos": [172, 190]}, {"entity": "David A. McAllester", "entity_type": "researcher", "pos": [193, 212]}, {"entity": "Richard S. Sutton", "entity_type": "researcher", "pos": [219, 236]}, {"entity": "Secure Systems Research department", "entity_type": "organization", "pos": [239, 273]}, {"entity": "Machine Learning", "entity_type": "field", "pos": [280, 296]}, {"entity": "Michael Collins", "entity_type": "researcher", "pos": [329, 344]}], "task": "NER"}
{"text": "When data are unlabelled , supervised learning is not possible , and an unsupervised learning approach is required which attempts to find natural Cluster analysis to groups , and then map new data to these formed groups .", "entity": [{"entity": "supervised learning", "entity_type": "field", "pos": [27, 46]}, {"entity": "unsupervised learning", "entity_type": "field", "pos": [72, 93]}, {"entity": "Cluster analysis", "entity_type": "task", "pos": [146, 162]}], "task": "NER"}
{"text": "This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab , originally as a branch of artificial intelligence and robotics .", "entity": [{"entity": "computer science", "entity_type": "field", "pos": [14, 30]}, {"entity": "MIT A.I. Lab", "entity_type": "organization", "pos": [91, 103]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [132, 155]}, {"entity": "robotics", "entity_type": "field", "pos": [160, 168]}], "task": "NER"}
{"text": "It could also be replaced by the Log loss equation below :", "entity": [{"entity": "Log loss", "entity_type": "metrics", "pos": [33, 41]}], "task": "NER"}
{"text": "The Shirley Ryan AbilityLab ( formerly the Rehabilitation Institute of Chicago ) , University of California at Berkeley , MIT , Stanford University , and University of Twente in the Netherlands are the researching leaders in biomechatronics .", "entity": [{"entity": "Shirley Ryan AbilityLab", "entity_type": "organization", "pos": [4, 27]}, {"entity": "Rehabilitation Institute of Chicago", "entity_type": "organization", "pos": [43, 78]}, {"entity": "University of California at Berkeley", "entity_type": "university", "pos": [83, 119]}, {"entity": "MIT", "entity_type": "university", "pos": [122, 125]}, {"entity": "Stanford University", "entity_type": "university", "pos": [128, 147]}, {"entity": "University of Twente", "entity_type": "university", "pos": [154, 174]}, {"entity": "Netherlands", "entity_type": "country", "pos": [182, 193]}, {"entity": "biomechatronics", "entity_type": "field", "pos": [225, 240]}], "task": "NER"}
{"text": "Given a set of predicted values and a corresponding set of actual values for X for various time periods , a common evaluation technique is to use the mean squared prediction error ; other measures are also available ( see forecasting # forecasting accuracy ) .", "entity": [{"entity": "mean squared prediction error", "entity_type": "metrics", "pos": [150, 179]}, {"entity": "forecasting accuracy", "entity_type": "metrics", "pos": [236, 256]}], "task": "NER"}
{"text": "Other measures , such as the proportion of correct predictions ( also termed accuracy ) , are not useful when the two classes are of very different sizes .", "entity": [{"entity": "accuracy", "entity_type": "metrics", "pos": [77, 85]}], "task": "NER"}
{"text": "The first alpha version of OpenCV was released to the public at the Conference on Computer Vision and Pattern Recognition in 2000 , and five betas were released between 2001 and 2005 .", "entity": [{"entity": "OpenCV", "entity_type": "product", "pos": [27, 33]}, {"entity": "Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [82, 121]}], "task": "NER"}
{"text": "Results have been presented which give correlation of up to 0.964 with human judgement at the corpus level , compared to BLEU ' s achievement of 0.817 on the same data set .", "entity": [{"entity": "BLEU", "entity_type": "metrics", "pos": [121, 125]}], "task": "NER"}
{"text": "An early version of VMAF has been shown to outperform other image and video quality metrics such as SSIM , PSNR -HVS and VQM-VFD on three of four datasets in terms of prediction accuracy , when compared to subjective ratings .", "entity": [{"entity": "VMAF", "entity_type": "metrics", "pos": [20, 24]}, {"entity": "SSIM", "entity_type": "metrics", "pos": [100, 104]}, {"entity": "PSNR -HVS", "entity_type": "metrics", "pos": [107, 116]}, {"entity": "VQM-VFD", "entity_type": "metrics", "pos": [121, 128]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [178, 186]}], "task": "NER"}
{"text": "For example , the ambiguity of ' mouse ' ( animal or device ) is not relevant in machine translation , but is relevant in information retrieval .", "entity": [{"entity": "machine translation", "entity_type": "task", "pos": [81, 100]}, {"entity": "information retrieval", "entity_type": "task", "pos": [122, 143]}], "task": "NER"}
{"text": "Geometric hashing was originally suggested in computer vision for object recognition in 2D and 3D ,", "entity": [{"entity": "Geometric hashing", "entity_type": "algorithm", "pos": [0, 17]}, {"entity": "computer vision", "entity_type": "field", "pos": [46, 61]}, {"entity": "object recognition", "entity_type": "task", "pos": [66, 84]}], "task": "NER"}
{"text": "It forms one of the three main categories of machine learning , along with supervised learning and reinforcement learning .", "entity": [{"entity": "machine learning", "entity_type": "field", "pos": [45, 61]}, {"entity": "supervised learning", "entity_type": "field", "pos": [75, 94]}, {"entity": "reinforcement learning", "entity_type": "field", "pos": [99, 121]}], "task": "NER"}
{"text": "Reinforcement learning , due to its generality , is studied in many other disciplines , such as game , control theory , operations research , information theory , simulation-based optimization , multi-agent systems , swarm intelligence , statistics and genetic algorithm s .", "entity": [{"entity": "Reinforcement learning", "entity_type": "field", "pos": [0, 22]}, {"entity": "game", "entity_type": "field", "pos": [96, 100]}, {"entity": "control theory", "entity_type": "field", "pos": [103, 117]}, {"entity": "operations research", "entity_type": "field", "pos": [120, 139]}, {"entity": "information theory", "entity_type": "field", "pos": [142, 160]}, {"entity": "simulation-based optimization", "entity_type": "field", "pos": [163, 192]}, {"entity": "multi-agent systems", "entity_type": "field", "pos": [195, 214]}, {"entity": "swarm intelligence", "entity_type": "field", "pos": [217, 235]}, {"entity": "statistics", "entity_type": "field", "pos": [238, 248]}, {"entity": "genetic algorithm", "entity_type": "algorithm", "pos": [253, 270]}], "task": "NER"}
{"text": "Pattern recognition is closely related to artificial intelligence and machine learning ,", "entity": [{"entity": "Pattern recognition", "entity_type": "field", "pos": [0, 19]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [42, 65]}, {"entity": "machine learning", "entity_type": "field", "pos": [70, 86]}], "task": "NER"}
{"text": "The software is used to design , train and deploy neural network ( supervised learning and unsupervised learning ) models to perform a wide variety of tasks such as data mining , classification , function approximation , multivariate regression and time-series prediction .", "entity": [{"entity": "neural network", "entity_type": "algorithm", "pos": [50, 64]}, {"entity": "supervised learning", "entity_type": "field", "pos": [67, 86]}, {"entity": "unsupervised learning", "entity_type": "field", "pos": [91, 112]}, {"entity": "data mining", "entity_type": "field", "pos": [165, 176]}, {"entity": "classification", "entity_type": "task", "pos": [179, 193]}, {"entity": "function approximation", "entity_type": "task", "pos": [196, 218]}, {"entity": "multivariate regression", "entity_type": "algorithm", "pos": [221, 244]}, {"entity": "time-series prediction", "entity_type": "task", "pos": [249, 271]}], "task": "NER"}
{"text": "In 2016 , he was elected Fellow of Association for the Advancement of Artificial Intelligence .", "entity": [{"entity": "Association for the Advancement of Artificial Intelligence", "entity_type": "conference", "pos": [35, 93]}], "task": "NER"}
{"text": "She serves as a member of the National Academy of Sciences ( since 2005 ) , American Academy of Arts and Sciences ( since 2009 ) ,", "entity": [{"entity": "National Academy of Sciences", "entity_type": "organization", "pos": [30, 58]}, {"entity": "American Academy of Arts and Sciences", "entity_type": "organization", "pos": [76, 113]}], "task": "NER"}
{"text": "During the 1973 Yom Kippur War , Soviet-supplied surface-to-air missile batteries in Egypt and Syria caused heavy damage Israeli fighter jet s .", "entity": [{"entity": "Yom Kippur War", "entity_type": "else", "pos": [16, 30]}, {"entity": "surface-to-air missile", "entity_type": "product", "pos": [49, 71]}, {"entity": "Egypt", "entity_type": "country", "pos": [85, 90]}, {"entity": "Syria", "entity_type": "country", "pos": [95, 100]}, {"entity": "Israeli", "entity_type": "else", "pos": [121, 128]}], "task": "NER"}
{"text": "Another resource ( free but copyrighted ) is the HTK book ( and the accompanying HTK toolkit ) .", "entity": [{"entity": "HTK book", "entity_type": "product", "pos": [49, 57]}, {"entity": "HTK toolkit", "entity_type": "product", "pos": [81, 92]}], "task": "NER"}
{"text": "- were taken in the 2004 AAAI Spring Symposium where linguists , computer scientists , and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect , appeal , subjectivity , and sentiment in text .", "entity": [{"entity": "2004 AAAI", "entity_type": "conference", "pos": [20, 29]}], "task": "NER"}
{"text": "A single grid can be analysed for both content ( eyeball inspection ) and structure ( cluster analysis , principal component analysis , and a variety of structural indices relating to the complexity and range of the ratings being the chief techniques used ) .", "entity": [{"entity": "eyeball inspection", "entity_type": "task", "pos": [49, 67]}, {"entity": "cluster analysis", "entity_type": "task", "pos": [86, 102]}, {"entity": "principal component analysis", "entity_type": "task", "pos": [105, 133]}], "task": "NER"}
{"text": "In 2018 Toyota was regarded as being behind in Self-driving car and in need of innovation .", "entity": [{"entity": "Toyota", "entity_type": "organization", "pos": [8, 14]}, {"entity": "Self-driving car", "entity_type": "product", "pos": [47, 63]}], "task": "NER"}
{"text": "Such targets include natural objects such as ground , sea , precipitation ( such as rain , snow or hail ) , sand storm s , animals ( especially birds ) , atmospheric turbulence , and other atmospheric effects , such as ionosphere reflections , meteor trails , and three body scatter spike .", "entity": [{"entity": "ionosphere reflections", "entity_type": "else", "pos": [219, 241]}, {"entity": "meteor trails", "entity_type": "else", "pos": [244, 257]}, {"entity": "three body scatter spike", "entity_type": "else", "pos": [264, 288]}], "task": "NER"}
{"text": "In planning and control , the essential difference between humanoids and other kinds of robots ( like industrial ones ) is that the movement of the robot must be human-like , using legged locomotion , especially biped gait .", "entity": [{"entity": "industrial", "entity_type": "product", "pos": [102, 112]}, {"entity": "biped gait", "entity_type": "else", "pos": [212, 222]}], "task": "NER"}
{"text": "The gradient descent can take many iterations to compute a local minimum with a required accuracy , if the curvature in different directions is very different for the given function .", "entity": [{"entity": "gradient descent", "entity_type": "algorithm", "pos": [4, 20]}, {"entity": "local minimum", "entity_type": "else", "pos": [59, 72]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [89, 97]}, {"entity": "curvature", "entity_type": "else", "pos": [107, 116]}], "task": "NER"}
{"text": "The 1997 RoboCup 2D Soccer Simulation League was the first RoboCup competition promoted in conjunction with International Joint Conference on Artificial Intelligence held in Nagoya , Japan , from 23 to 29 August 1997 .", "entity": [{"entity": "1997 RoboCup 2D Soccer Simulation League", "entity_type": "else", "pos": [4, 44]}, {"entity": "RoboCup", "entity_type": "else", "pos": [59, 66]}, {"entity": "International Joint Conference on Artificial Intelligence", "entity_type": "conference", "pos": [108, 165]}, {"entity": "Nagoya", "entity_type": "location", "pos": [174, 180]}, {"entity": "Japan", "entity_type": "country", "pos": [183, 188]}], "task": "NER"}
{"text": "Other programming options include an embedded Python environment , and an R Console plus support for Rserve .", "entity": [{"entity": "Python", "entity_type": "program language", "pos": [46, 52]}, {"entity": "R", "entity_type": "program language", "pos": [74, 75]}, {"entity": "Rserve", "entity_type": "product", "pos": [101, 107]}], "task": "NER"}
{"text": "From Bonn he has contributed fundamentally to artificial intelligence and robotics ( with Wolfram Burgard , Dieter Fox , Sebastian Thrun among his students ) , and to the development of software engineering , particularly in civil engineering , and information systems , particularly in the geosciences. won the AAAI Classic Paper award of 2016.2014 .", "entity": [{"entity": "Bonn", "entity_type": "researcher", "pos": [5, 9]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [46, 69]}, {"entity": "robotics", "entity_type": "field", "pos": [74, 82]}, {"entity": "Wolfram Burgard", "entity_type": "researcher", "pos": [90, 105]}, {"entity": "Dieter Fox", "entity_type": "researcher", "pos": [108, 118]}, {"entity": "Sebastian Thrun", "entity_type": "researcher", "pos": [121, 136]}, {"entity": "software engineering", "entity_type": "field", "pos": [186, 206]}, {"entity": "civil engineering", "entity_type": "field", "pos": [225, 242]}, {"entity": "information systems", "entity_type": "field", "pos": [249, 268]}, {"entity": "geosciences.", "entity_type": "field", "pos": [291, 303]}, {"entity": "AAAI Classic Paper award", "entity_type": "else", "pos": [312, 336]}], "task": "NER"}
{"text": "The first USA edition of Campus Party will take place from 20 to 22 of August at TCF Center in Detroit , Michigan .", "entity": [{"entity": "USA edition of Campus Party", "entity_type": "conference", "pos": [10, 37]}, {"entity": "TCF Center", "entity_type": "location", "pos": [81, 91]}, {"entity": "Detroit", "entity_type": "location", "pos": [95, 102]}, {"entity": "Michigan", "entity_type": "location", "pos": [105, 113]}], "task": "NER"}
{"text": "Together with Yann LeCun , and Yoshua Bengio , Hinton won the 2018 Turing Award for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing .", "entity": [{"entity": "Yann LeCun", "entity_type": "researcher", "pos": [14, 24]}, {"entity": "Yoshua Bengio", "entity_type": "researcher", "pos": [31, 44]}, {"entity": "Hinton", "entity_type": "researcher", "pos": [47, 53]}, {"entity": "Turing Award", "entity_type": "else", "pos": [67, 79]}, {"entity": "deep neural networks", "entity_type": "algorithm", "pos": [140, 160]}], "task": "NER"}
{"text": "Euler Math Toolbox uses a matrix language similar to MATLAB , a system that had been under development since the 1970s .", "entity": [{"entity": "Euler Math Toolbox", "entity_type": "product", "pos": [0, 18]}, {"entity": "MATLAB", "entity_type": "product", "pos": [53, 59]}], "task": "NER"}
{"text": "Some languages make it possible portably ( e.g. Scheme , Common Lisp , Perl or D ) .", "entity": [{"entity": "Scheme", "entity_type": "program language", "pos": [48, 54]}, {"entity": "Common Lisp", "entity_type": "program language", "pos": [57, 68]}, {"entity": "Perl", "entity_type": "program language", "pos": [71, 75]}, {"entity": "D", "entity_type": "program language", "pos": [79, 80]}], "task": "NER"}
{"text": "In 1969 a famous book entitled Perceptrons by Marvin Minsky and Seymour Papert showed that it was impossible for these classes of network to learn an XOR function .", "entity": [{"entity": "Perceptrons", "entity_type": "else", "pos": [31, 42]}, {"entity": "Marvin Minsky", "entity_type": "researcher", "pos": [46, 59]}, {"entity": "Seymour Papert", "entity_type": "researcher", "pos": [64, 78]}, {"entity": "XOR function", "entity_type": "else", "pos": [150, 162]}], "task": "NER"}
{"text": "Large numbers of Russian scientific and technical documents were translated using SYSTRAN under the auspices of the USAF Foreign Technology Division ( later the National Air and Space Intelligence Center ) at Wright-Patterson Air Force Base , Ohio .", "entity": [{"entity": "Russian", "entity_type": "else", "pos": [17, 24]}, {"entity": "SYSTRAN", "entity_type": "product", "pos": [82, 89]}, {"entity": "USAF Foreign Technology Division", "entity_type": "organization", "pos": [116, 148]}, {"entity": "National Air and Space Intelligence Center", "entity_type": "organization", "pos": [161, 203]}, {"entity": "Wright-Patterson Air Force Base", "entity_type": "location", "pos": [209, 240]}, {"entity": "Ohio", "entity_type": "location", "pos": [243, 247]}], "task": "NER"}
{"text": "Semi-supervised learning falls between unsupervised learning ( without any labeled training data ) and supervised learning ( with completely labeled training data ) .", "entity": [{"entity": "Semi-supervised learning", "entity_type": "field", "pos": [0, 24]}, {"entity": "unsupervised learning", "entity_type": "field", "pos": [39, 60]}, {"entity": "supervised learning", "entity_type": "field", "pos": [103, 122]}], "task": "NER"}
{"text": "An n -gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of a ( n − 1 ) -order Markov model .efficiently .", "entity": [{"entity": "n -gram model", "entity_type": "algorithm", "pos": [3, 16]}, {"entity": "probabilistic language model", "entity_type": "algorithm", "pos": [30, 58]}, {"entity": "Markov model", "entity_type": "algorithm", "pos": [141, 153]}], "task": "NER"}
{"text": "The Cleveland Clinic has used Cyc to develop a natural language query interface of biomedical information , spanning decades of information on cardiothoracic surgeries .", "entity": [{"entity": "Cleveland Clinic", "entity_type": "location", "pos": [4, 20]}, {"entity": "Cyc", "entity_type": "product", "pos": [30, 33]}, {"entity": "natural language query interface of biomedical information", "entity_type": "product", "pos": [47, 105]}], "task": "NER"}
{"text": "The incident strained relations between the United States and Japan , and resulted in the arrest and prosecution two senior executives , as well as the imposition of sanctions on the company by both countries .", "entity": [{"entity": "United States", "entity_type": "country", "pos": [44, 57]}, {"entity": "Japan", "entity_type": "country", "pos": [62, 67]}], "task": "NER"}
{"text": "If the modeling is done by an artificial neural network or other machine learning , the optimization of parameters is called training , while the optimization of model hyperparameters is called tuning and often uses cross-validation ..", "entity": [{"entity": "artificial neural network", "entity_type": "algorithm", "pos": [30, 55]}, {"entity": "machine learning", "entity_type": "field", "pos": [65, 81]}, {"entity": "training", "entity_type": "else", "pos": [125, 133]}, {"entity": "tuning", "entity_type": "else", "pos": [194, 200]}, {"entity": "cross-validation", "entity_type": "algorithm", "pos": [216, 232]}], "task": "NER"}
{"text": "Localized versions of the site available in the United Kingdom , India , and Australia were discontinued following the acquisition of Rotten Tomatoes by Fandango .", "entity": [{"entity": "United Kingdom", "entity_type": "country", "pos": [48, 62]}, {"entity": "India", "entity_type": "country", "pos": [65, 70]}, {"entity": "Australia", "entity_type": "country", "pos": [77, 86]}, {"entity": "Rotten Tomatoes", "entity_type": "organization", "pos": [134, 149]}, {"entity": "Fandango", "entity_type": "organization", "pos": [153, 161]}], "task": "NER"}
{"text": "The NER model is one of a number of methods for determining the accuracy of live subtitles in television broadcasts and events that are produced using speech recognition .", "entity": [{"entity": "NER", "entity_type": "task", "pos": [4, 7]}, {"entity": "accuracy", "entity_type": "metrics", "pos": [64, 72]}, {"entity": "speech recognition", "entity_type": "task", "pos": [151, 169]}], "task": "NER"}
{"text": "Atran has taught at Cambridge University , Hebrew University in Jerusalem , the École pratique des hautes études and École Polytechnique in Paris , and John Jay College of Criminal Justice in New York City .", "entity": [{"entity": "Atran", "entity_type": "researcher", "pos": [0, 5]}, {"entity": "Cambridge University", "entity_type": "university", "pos": [20, 40]}, {"entity": "Hebrew University", "entity_type": "university", "pos": [43, 60]}, {"entity": "Jerusalem", "entity_type": "location", "pos": [64, 73]}, {"entity": "École pratique des hautes études", "entity_type": "university", "pos": [80, 112]}, {"entity": "École Polytechnique", "entity_type": "university", "pos": [117, 136]}, {"entity": "Paris", "entity_type": "location", "pos": [140, 145]}, {"entity": "John Jay College of Criminal Justice", "entity_type": "university", "pos": [152, 188]}, {"entity": "New York City", "entity_type": "location", "pos": [192, 205]}], "task": "NER"}
{"text": "SHRDLU was an early natural language understanding computer program , developed by Terry Winograd at MIT in 1968-1970", "entity": [{"entity": "SHRDLU", "entity_type": "product", "pos": [0, 6]}, {"entity": "natural language understanding", "entity_type": "task", "pos": [20, 50]}, {"entity": "Terry Winograd", "entity_type": "researcher", "pos": [83, 97]}, {"entity": "MIT", "entity_type": "university", "pos": [101, 104]}], "task": "NER"}
{"text": "He received a B.E. in electronics engineering from B.M.S. College of Engineering in Bangalore , India in 1982 , when it was affiliated with Bangalore University , an M.S. in electrical and computer engineering in 1984 from Drexel University , and an M.S. in computer science in 1989 , and a Ph.D. in 1990 , respectively , from the University of Wisconsin-Madison , where he studied Artificial Intelligence and worked with Leonard Uhr .", "entity": [{"entity": "B.E.", "entity_type": "else", "pos": [14, 18]}, {"entity": "electronics engineering", "entity_type": "field", "pos": [22, 45]}, {"entity": "B.M.S. College of Engineering", "entity_type": "university", "pos": [51, 80]}, {"entity": "Bangalore", "entity_type": "location", "pos": [84, 93]}, {"entity": "India", "entity_type": "country", "pos": [96, 101]}, {"entity": "Bangalore University", "entity_type": "university", "pos": [140, 160]}, {"entity": "M.S.", "entity_type": "else", "pos": [166, 170]}, {"entity": "electrical and computer engineering", "entity_type": "field", "pos": [174, 209]}, {"entity": "Drexel University", "entity_type": "university", "pos": [223, 240]}, {"entity": "M.S.", "entity_type": "else", "pos": [250, 254]}, {"entity": "computer science", "entity_type": "field", "pos": [258, 274]}, {"entity": "Ph.D.", "entity_type": "else", "pos": [291, 296]}, {"entity": "University of Wisconsin-Madison", "entity_type": "university", "pos": [331, 362]}, {"entity": "Artificial Intelligence", "entity_type": "field", "pos": [382, 405]}, {"entity": "Leonard Uhr", "entity_type": "researcher", "pos": [422, 433]}], "task": "NER"}
{"text": "Accuracy is usually rated with word error rate ( WER ) , whereas speed is measured with the real time factor .", "entity": [{"entity": "word error rate", "entity_type": "metrics", "pos": [31, 46]}, {"entity": "WER", "entity_type": "metrics", "pos": [49, 52]}, {"entity": "real time factor", "entity_type": "metrics", "pos": [92, 108]}], "task": "NER"}
{"text": "In 1971 Terry Winograd developed an early natural language processing engine capable of interpreting naturally written commands within a simple rule-governed environment .", "entity": [{"entity": "Terry Winograd", "entity_type": "researcher", "pos": [8, 22]}, {"entity": "natural language processing", "entity_type": "field", "pos": [42, 69]}], "task": "NER"}
{"text": "In artificial intelligence , Marvin Minsky , Herbert A. Simon , and Allen Newell are prominent .", "entity": [{"entity": "artificial intelligence", "entity_type": "field", "pos": [3, 26]}, {"entity": "Marvin Minsky", "entity_type": "researcher", "pos": [29, 42]}, {"entity": "Herbert A. Simon", "entity_type": "researcher", "pos": [45, 61]}, {"entity": "Allen Newell", "entity_type": "researcher", "pos": [68, 80]}], "task": "NER"}
{"text": "In the latter half of the 20th century , electrical engineering itself separated into several disciplines , specialising in the design and analysis of systems that manipulate physical signals ; electronic engineering and computer engineering as examples ; while design engineering developed to deal with functional design of user-machine interfaces .", "entity": [{"entity": "electrical engineering", "entity_type": "field", "pos": [41, 63]}, {"entity": "electronic engineering", "entity_type": "field", "pos": [194, 216]}, {"entity": "computer engineering", "entity_type": "field", "pos": [221, 241]}, {"entity": "design engineering", "entity_type": "field", "pos": [262, 280]}, {"entity": "user-machine interfaces", "entity_type": "else", "pos": [325, 348]}], "task": "NER"}
{"text": "Perhaps the simplest statistic is accuracy or Fraction Correct ( FC ) , which measures the fraction of all instances that are correctly categorized ; it is the ratio of the number of correct classifications to the total number of correct or incorrect classifications : ( TP + TN ) / Total Population = ( TP + TN ) / ( TP + TN + FP + FN ) .", "entity": [{"entity": "accuracy", "entity_type": "metrics", "pos": [34, 42]}, {"entity": "Fraction Correct", "entity_type": "metrics", "pos": [46, 62]}, {"entity": "FC", "entity_type": "metrics", "pos": [65, 67]}, {"entity": "TP + TN", "entity_type": "metrics", "pos": [271, 278]}, {"entity": "TP + TN", "entity_type": "metrics", "pos": [304, 311]}, {"entity": "TP + TN + FP + FN", "entity_type": "metrics", "pos": [318, 335]}], "task": "NER"}
{"text": "In the academic community , the major forums for research started in 1995 when the First International Conference Data Mining and Knowledge Discovery ( KDD-95 ) was started in Montreal under AAAI sponsorship .", "entity": [{"entity": "First International Conference Data Mining and Knowledge Discovery", "entity_type": "conference", "pos": [83, 149]}, {"entity": "KDD-95", "entity_type": "conference", "pos": [152, 158]}, {"entity": "Montreal", "entity_type": "location", "pos": [176, 184]}, {"entity": "AAAI", "entity_type": "conference", "pos": [191, 195]}], "task": "NER"}
{"text": "In this approach , models are developed using different data mining , machine learning algorithms to predict users ' rating of unrated items .", "entity": [{"entity": "data mining", "entity_type": "field", "pos": [56, 67]}, {"entity": "machine learning", "entity_type": "field", "pos": [70, 86]}], "task": "NER"}
{"text": "In light of the above discussion , we see that the SVM technique is equivalent to empirical risk with Tikhonov regularization , where in this case the loss function is the hinge loss", "entity": [{"entity": "SVM", "entity_type": "algorithm", "pos": [51, 54]}, {"entity": "empirical risk", "entity_type": "algorithm", "pos": [82, 96]}, {"entity": "Tikhonov regularization", "entity_type": "algorithm", "pos": [102, 125]}, {"entity": "loss function", "entity_type": "else", "pos": [151, 164]}], "task": "NER"}
{"text": "The 2015 edition was hosted by Molly McGrath , with Chris Rose and former UFC fighter Kenny Florian as commentators .", "entity": [{"entity": "Molly McGrath", "entity_type": "person", "pos": [31, 44]}, {"entity": "Chris Rose", "entity_type": "person", "pos": [52, 62]}, {"entity": "UFC", "entity_type": "organization", "pos": [74, 77]}, {"entity": "Kenny Florian", "entity_type": "person", "pos": [86, 99]}], "task": "NER"}
{"text": "A subset called Micro-Planner was implemented by Gerald Jay Sussman , Eugene Charniak and Terry Winograd Sussman , , and Winograd 1971 and was used in Winograd 's natural-language understanding program SHRDLU , Eugene Charniak 's story understanding work , Thorne McCarty 's work on legal reasoning , and some other projects .", "entity": [{"entity": "Micro-Planner", "entity_type": "product", "pos": [16, 29]}, {"entity": "Gerald Jay Sussman", "entity_type": "researcher", "pos": [49, 67]}, {"entity": "Eugene Charniak", "entity_type": "researcher", "pos": [70, 85]}, {"entity": "Terry Winograd", "entity_type": "researcher", "pos": [90, 104]}, {"entity": "Sussman", "entity_type": "researcher", "pos": [105, 112]}, {"entity": "Winograd", "entity_type": "researcher", "pos": [121, 129]}, {"entity": "Winograd", "entity_type": "researcher", "pos": [151, 159]}, {"entity": "natural-language understanding", "entity_type": "task", "pos": [163, 193]}, {"entity": "SHRDLU", "entity_type": "product", "pos": [202, 208]}, {"entity": "Eugene Charniak", "entity_type": "researcher", "pos": [211, 226]}, {"entity": "story understanding", "entity_type": "task", "pos": [230, 249]}, {"entity": "Thorne McCarty", "entity_type": "researcher", "pos": [257, 271]}, {"entity": "legal reasoning", "entity_type": "task", "pos": [283, 298]}], "task": "NER"}
{"text": "WordNet has been used for a number of purposes in information systems , including word-sense disambiguation , information retrieval , automatic text classification , Automatic summarization , machine translation and even automatic crossword puzzle generation .", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [0, 7]}, {"entity": "information systems", "entity_type": "product", "pos": [50, 69]}, {"entity": "word-sense disambiguation", "entity_type": "task", "pos": [82, 107]}, {"entity": "information retrieval", "entity_type": "task", "pos": [110, 131]}, {"entity": "automatic text classification", "entity_type": "task", "pos": [134, 163]}, {"entity": "Automatic summarization", "entity_type": "task", "pos": [166, 189]}, {"entity": "machine translation", "entity_type": "task", "pos": [192, 211]}, {"entity": "automatic crossword puzzle generation", "entity_type": "task", "pos": [221, 258]}], "task": "NER"}
{"text": "Keutzer was named a Fellow of the IEEE in 1996 .", "entity": [{"entity": "Keutzer", "entity_type": "researcher", "pos": [0, 7]}, {"entity": "IEEE", "entity_type": "organization", "pos": [34, 38]}], "task": "NER"}
{"text": "A widely used type of composition is the nonlinear weighted sum , where math \\ textstyle f ( x ) = K \\ left ( \\ sum _ i w _ i g _ i ( x ) \\ right ) / math , where math \\ textstyle K / math ( commonly referred to as the activation function ) is some predefined function , such as the hyperbolic tangent , sigmoid function , softmax function , or rectifier function .", "entity": [{"entity": "nonlinear weighted sum", "entity_type": "algorithm", "pos": [41, 63]}, {"entity": "activation function", "entity_type": "else", "pos": [219, 238]}, {"entity": "hyperbolic tangent", "entity_type": "algorithm", "pos": [283, 301]}, {"entity": "sigmoid function", "entity_type": "algorithm", "pos": [304, 320]}, {"entity": "softmax function", "entity_type": "algorithm", "pos": [323, 339]}, {"entity": "rectifier function", "entity_type": "algorithm", "pos": [345, 363]}], "task": "NER"}
{"text": "In the film Westworld , female robots actually engaged in intercourse with human men as part of the make-believe vacation world human customers paid to attend .", "entity": [{"entity": "Westworld", "entity_type": "else", "pos": [12, 21]}], "task": "NER"}
{"text": "Typically , the process starts by terminology extraction and concepts or noun phrase s from plain text using linguistic processors such as part-of-speech tagging and phrase chunking .", "entity": [{"entity": "terminology extraction", "entity_type": "task", "pos": [34, 56]}, {"entity": "part-of-speech tagging", "entity_type": "task", "pos": [139, 161]}, {"entity": "phrase chunking", "entity_type": "task", "pos": [166, 181]}], "task": "NER"}
{"text": "They demonstrated its performance on a number of problems of interest to the machine learning community , including handwriting recognition .", "entity": [{"entity": "machine learning", "entity_type": "field", "pos": [77, 93]}, {"entity": "handwriting recognition", "entity_type": "task", "pos": [116, 139]}], "task": "NER"}
{"text": "While studying at Stanford , Scheinman was awarded a fellowship sponsored by George Devol , the inventor of the Unimate , the first industrial robot .", "entity": [{"entity": "Stanford", "entity_type": "university", "pos": [18, 26]}, {"entity": "Scheinman", "entity_type": "researcher", "pos": [29, 38]}, {"entity": "George Devol", "entity_type": "researcher", "pos": [77, 89]}, {"entity": "Unimate", "entity_type": "product", "pos": [112, 119]}, {"entity": "industrial robot", "entity_type": "product", "pos": [132, 148]}], "task": "NER"}
{"text": "While originally used to evaluate machine translations , bilingual evaluation understudy ( BLEU ) has been used successfully to evaluate paraphrase generation models as well .", "entity": [{"entity": "machine translations", "entity_type": "task", "pos": [34, 54]}, {"entity": "bilingual evaluation understudy", "entity_type": "metrics", "pos": [57, 88]}, {"entity": "BLEU", "entity_type": "metrics", "pos": [91, 95]}, {"entity": "paraphrase generation models", "entity_type": "product", "pos": [137, 165]}], "task": "NER"}
{"text": "Unimation later licensed their technology to Kawasaki Heavy Industries and GKN , manufacturing Unimate s in Japan and England respectively .", "entity": [{"entity": "Unimation", "entity_type": "organization", "pos": [0, 9]}, {"entity": "Kawasaki Heavy Industries", "entity_type": "organization", "pos": [45, 70]}, {"entity": "GKN", "entity_type": "organization", "pos": [75, 78]}, {"entity": "Unimate", "entity_type": "product", "pos": [95, 102]}, {"entity": "Japan", "entity_type": "country", "pos": [108, 113]}, {"entity": "England", "entity_type": "country", "pos": [118, 125]}], "task": "NER"}
{"text": "Much of the confusion between these two research communities ( which do often have separate conferences and separate journals , ECML PKDD being a major exception ) comes from the basic assumptions they work with : in machine learning , performance is usually evaluated with respect to the ability to reproduce known knowledge , while in knowledge discovery and data mining ( KDD ) the key task is the discovery of previously unknown knowledge .", "entity": [{"entity": "ECML PKDD", "entity_type": "conference", "pos": [128, 137]}, {"entity": "machine learning", "entity_type": "field", "pos": [217, 233]}, {"entity": "knowledge discovery and data mining", "entity_type": "conference", "pos": [337, 372]}, {"entity": "KDD", "entity_type": "conference", "pos": [375, 378]}], "task": "NER"}
{"text": "Hidden Markov model s are the basis for most modern automatic speech recognition systems .", "entity": [{"entity": "Hidden Markov model", "entity_type": "algorithm", "pos": [0, 19]}, {"entity": "automatic speech recognition systems", "entity_type": "product", "pos": [52, 88]}], "task": "NER"}
{"text": ", a company in Bangalore , India specializing in online handwriting recognition software .", "entity": [{"entity": "Bangalore", "entity_type": "location", "pos": [15, 24]}, {"entity": "India", "entity_type": "country", "pos": [27, 32]}, {"entity": "handwriting recognition", "entity_type": "task", "pos": [56, 79]}], "task": "NER"}
{"text": "Do repeated translations converge on a single expression in both languages ? I.e. does the translation method show stationarity or produce a canonical form ? Does the translation become stationary without losing the original meaning ? This metric has been criticized as not being well correlated with BLEU ( BiLingual Evaluation Understudy ) scores .", "entity": [{"entity": "canonical form", "entity_type": "else", "pos": [141, 155]}, {"entity": "BLEU", "entity_type": "metrics", "pos": [301, 305]}, {"entity": "BiLingual Evaluation Understudy", "entity_type": "metrics", "pos": [308, 339]}], "task": "NER"}
{"text": "He holds fellowships in the American Association for Artificial Intelligence , the Center for Advanced Study in the Behavioral Sciences at Stanford University , the MIT Center for Cognitive Science , the Canadian Institute for Advanced Research , the Canadian Psychological Association , and was elected Fellow of the Royal Society of Canada in 1998 .", "entity": [{"entity": "American Association for Artificial Intelligence", "entity_type": "conference", "pos": [28, 76]}, {"entity": "Center for Advanced Study in the Behavioral Sciences", "entity_type": "organization", "pos": [83, 135]}, {"entity": "Stanford University", "entity_type": "university", "pos": [139, 158]}, {"entity": "MIT", "entity_type": "university", "pos": [165, 168]}, {"entity": "Cognitive Science", "entity_type": "field", "pos": [180, 197]}, {"entity": "Canadian Institute for Advanced Research", "entity_type": "organization", "pos": [204, 244]}, {"entity": "Canadian Psychological Association", "entity_type": "organization", "pos": [251, 285]}, {"entity": "Royal Society of Canada", "entity_type": "organization", "pos": [318, 341]}], "task": "NER"}
{"text": "Hinton - together with Yoshua Bengio and Yann LeCun - are referred to by some as the Godfathers of AI and Godfathers of Deep Learning .", "entity": [{"entity": "Hinton", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "Yoshua Bengio", "entity_type": "researcher", "pos": [23, 36]}, {"entity": "Yann LeCun", "entity_type": "researcher", "pos": [41, 51]}, {"entity": "Godfathers of AI", "entity_type": "else", "pos": [85, 101]}, {"entity": "Godfathers of Deep Learning", "entity_type": "else", "pos": [106, 133]}], "task": "NER"}
{"text": "The lightweight open-source speech project eSpeak , which has its own approach to synthesis , has experimented with Mandarin and Cantonese. eSpeak was used by Google Translate from May 20102010 .", "entity": [{"entity": "eSpeak", "entity_type": "product", "pos": [43, 49]}, {"entity": "Mandarin", "entity_type": "else", "pos": [116, 124]}, {"entity": "Cantonese.", "entity_type": "else", "pos": [129, 139]}, {"entity": "eSpeak", "entity_type": "product", "pos": [140, 146]}, {"entity": "Google Translate", "entity_type": "product", "pos": [159, 175]}], "task": "NER"}
{"text": "Also released in 1982 , Software Automatic Mouth was the first commercial all-software voice synthesis program .", "entity": [{"entity": "Software Automatic Mouth", "entity_type": "product", "pos": [24, 48]}, {"entity": "synthesis program", "entity_type": "task", "pos": [93, 110]}], "task": "NER"}
{"text": "The column ratios are TRUE Positive Rate ( TPR , aka Sensitivity or recall ) ( TP / ( TP + FN ) ) , with complement the FALSE Negative Rate ( FNR ) ( FN / ( TP + FN ) ) ; and TRUE Negative Rate ( TNR , aka Specificity , SPC ) ( TN / ( TN + FP ) ) , with complement FALSE Positive Rate ( FPR ) ( FP / ( TN + FP ) ) .", "entity": [{"entity": "TRUE Positive Rate", "entity_type": "metrics", "pos": [22, 40]}, {"entity": "TPR", "entity_type": "metrics", "pos": [43, 46]}, {"entity": "Sensitivity", "entity_type": "metrics", "pos": [53, 64]}, {"entity": "recall", "entity_type": "metrics", "pos": [68, 74]}, {"entity": "TP / ( TP + FN )", "entity_type": "metrics", "pos": [79, 95]}, {"entity": "FALSE Negative Rate", "entity_type": "metrics", "pos": [120, 139]}, {"entity": "FNR", "entity_type": "metrics", "pos": [142, 145]}, {"entity": "FN / ( TP + FN )", "entity_type": "metrics", "pos": [150, 166]}, {"entity": "TRUE Negative Rate", "entity_type": "metrics", "pos": [175, 193]}, {"entity": "TNR", "entity_type": "metrics", "pos": [196, 199]}, {"entity": "Specificity", "entity_type": "metrics", "pos": [206, 217]}, {"entity": "SPC", "entity_type": "metrics", "pos": [220, 223]}, {"entity": "TN / ( TN + FP )", "entity_type": "metrics", "pos": [228, 244]}, {"entity": "FALSE Positive Rate", "entity_type": "metrics", "pos": [265, 284]}, {"entity": "FPR", "entity_type": "metrics", "pos": [287, 290]}, {"entity": "FP / ( TN + FP )", "entity_type": "metrics", "pos": [295, 311]}], "task": "NER"}
{"text": "Edsinger and Weber collaborated on many other robots as well , and their experience working with the Kismet", "entity": [{"entity": "Edsinger", "entity_type": "person", "pos": [0, 8]}, {"entity": "Weber", "entity_type": "organization", "pos": [13, 18]}], "task": "NER"}
{"text": "R functionality is accessible from several scripting languages such as Python , are available as well .", "entity": [{"entity": "R", "entity_type": "program language", "pos": [0, 1]}, {"entity": "Python", "entity_type": "program language", "pos": [71, 77]}], "task": "NER"}
{"text": "VAL was one of the first robot languages and was used in Unimate robots .", "entity": [{"entity": "VAL", "entity_type": "program language", "pos": [0, 3]}, {"entity": "Unimate robots", "entity_type": "product", "pos": [57, 71]}], "task": "NER"}
{"text": "They presented their database for the first time as a poster at the 2009 Conference on Computer Vision and Pattern Recognition ( CVPR ) in Florida .", "entity": [{"entity": "2009 Conference on Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [68, 126]}, {"entity": "CVPR", "entity_type": "conference", "pos": [129, 133]}, {"entity": "Florida", "entity_type": "location", "pos": [139, 146]}], "task": "NER"}
{"text": "Categorization tasks in which no labels are supplied are referred to as unsupervised classification , unsupervised learning , Cluster analysis .", "entity": [{"entity": "Categorization tasks", "entity_type": "else", "pos": [0, 20]}, {"entity": "unsupervised classification", "entity_type": "task", "pos": [72, 99]}, {"entity": "unsupervised learning", "entity_type": "field", "pos": [102, 123]}, {"entity": "Cluster analysis", "entity_type": "task", "pos": [126, 142]}], "task": "NER"}
{"text": "It needs to Object recognition , recognize and locate humans and further emotion recognition .", "entity": [{"entity": "Object recognition", "entity_type": "task", "pos": [12, 30]}, {"entity": "emotion recognition", "entity_type": "task", "pos": [73, 92]}], "task": "NER"}
{"text": "The process is complex and contains encoding and recall or retrieval .", "entity": [{"entity": "encoding", "entity_type": "else", "pos": [36, 44]}, {"entity": "recall", "entity_type": "else", "pos": [49, 55]}, {"entity": "retrieval", "entity_type": "else", "pos": [59, 68]}], "task": "NER"}
{"text": "Also known as parallel robots , or generalized Stewart platforms ( in the Stewart platform , the actuators are paired together on both the basis and the platform ) , these systems are articulated robot s that use similar mechanisms for the movement of either the robot on its base , or one or more manipulator arms .", "entity": [{"entity": "Stewart platforms", "entity_type": "product", "pos": [47, 64]}, {"entity": "Stewart platform", "entity_type": "product", "pos": [74, 90]}, {"entity": "articulated robot", "entity_type": "product", "pos": [184, 201]}], "task": "NER"}
{"text": "Machine vision as a systems engineering discipline can be considered distinct from computer vision , a form of computer science .", "entity": [{"entity": "Machine vision", "entity_type": "field", "pos": [0, 14]}, {"entity": "systems engineering", "entity_type": "field", "pos": [20, 39]}, {"entity": "computer vision", "entity_type": "field", "pos": [83, 98]}, {"entity": "computer science", "entity_type": "field", "pos": [111, 127]}], "task": "NER"}
{"text": "The activation function of the LSTM gates is often the logistic sigmoid function .", "entity": [{"entity": "LSTM gates", "entity_type": "algorithm", "pos": [31, 41]}, {"entity": "logistic sigmoid function", "entity_type": "algorithm", "pos": [55, 80]}], "task": "NER"}
{"text": "In other words , the sample mean is the ( necessarily unique ) efficient estimator , and thus also the minimum variance unbiased estimator ( MVUE ) , in addition to being the maximum likelihood estimator .", "entity": [{"entity": "sample mean", "entity_type": "metrics", "pos": [21, 32]}, {"entity": "minimum variance unbiased estimator", "entity_type": "metrics", "pos": [103, 138]}, {"entity": "MVUE", "entity_type": "metrics", "pos": [141, 145]}, {"entity": "maximum likelihood estimator", "entity_type": "metrics", "pos": [175, 203]}], "task": "NER"}
{"text": "The 2001 Scientific American article by Berners-Lee , James Hendler , and Ora Lassila described an expected evolution of the existing Web to a Semantic Web .", "entity": [{"entity": "Scientific American", "entity_type": "else", "pos": [9, 28]}, {"entity": "Berners-Lee", "entity_type": "researcher", "pos": [40, 51]}, {"entity": "James Hendler", "entity_type": "researcher", "pos": [54, 67]}, {"entity": "Ora Lassila", "entity_type": "researcher", "pos": [74, 85]}, {"entity": "Web", "entity_type": "product", "pos": [134, 137]}, {"entity": "Semantic Web", "entity_type": "product", "pos": [143, 155]}], "task": "NER"}
{"text": "Blade Runner used a number of then-lesser-known actors : Sean Young portrays Rachael , an experimental replicant implanted with the memories of Tyrell 's niece , causing her to believe she is human ; Sammon , pp. 92-93 Nina Axelrod auditioned for the role .", "entity": [{"entity": "Blade Runner", "entity_type": "else", "pos": [0, 12]}, {"entity": "Sean Young", "entity_type": "person", "pos": [57, 67]}, {"entity": "Rachael", "entity_type": "person", "pos": [77, 84]}, {"entity": "Tyrell", "entity_type": "person", "pos": [144, 150]}, {"entity": "Sammon", "entity_type": "person", "pos": [200, 206]}, {"entity": "Nina Axelrod", "entity_type": "person", "pos": [219, 231]}], "task": "NER"}
{"text": "Gerry Sussman , Eugene Charniak , Seymour Papert and Terry Winograd visited the University of Edinburgh in 1971 spreading the news about Micro-Planner and SHRDLU and casting doubt on the resolution uniform proof procedure approach that had been the mainstay of the Edinburgh Logicists .", "entity": [{"entity": "Gerry Sussman", "entity_type": "researcher", "pos": [0, 13]}, {"entity": "Eugene Charniak", "entity_type": "researcher", "pos": [16, 31]}, {"entity": "Seymour Papert", "entity_type": "researcher", "pos": [34, 48]}, {"entity": "Terry Winograd", "entity_type": "researcher", "pos": [53, 67]}, {"entity": "University of Edinburgh", "entity_type": "university", "pos": [80, 103]}, {"entity": "Micro-Planner", "entity_type": "product", "pos": [137, 150]}, {"entity": "SHRDLU", "entity_type": "product", "pos": [155, 161]}, {"entity": "Edinburgh", "entity_type": "location", "pos": [265, 274]}], "task": "NER"}
{"text": "Walter 's work inspired subsequent generations of robotics researchers such as Rodney Brooks , Hans Moravec and Mark Tilden .", "entity": [{"entity": "Walter", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "robotics", "entity_type": "field", "pos": [50, 58]}, {"entity": "Rodney Brooks", "entity_type": "researcher", "pos": [79, 92]}, {"entity": "Hans Moravec", "entity_type": "researcher", "pos": [95, 107]}, {"entity": "Mark Tilden", "entity_type": "researcher", "pos": [112, 123]}], "task": "NER"}
{"text": "Subsequently , a similar GPU-based CNN by Alex Krizhevsky et al. won the ImageNet Large Scale Visual Recognition Challenge 2012 .", "entity": [{"entity": "CNN", "entity_type": "algorithm", "pos": [35, 38]}, {"entity": "Alex Krizhevsky", "entity_type": "researcher", "pos": [42, 57]}, {"entity": "ImageNet Large Scale Visual Recognition Challenge 2012", "entity_type": "conference", "pos": [73, 127]}], "task": "NER"}
{"text": "Commonly used loss functions for probabilistic classification include log loss and the Brier score between the predicted and the TRUE probability distributions .", "entity": [{"entity": "loss functions", "entity_type": "else", "pos": [14, 28]}, {"entity": "log loss", "entity_type": "metrics", "pos": [70, 78]}, {"entity": "Brier score", "entity_type": "metrics", "pos": [87, 98]}, {"entity": "TRUE probability", "entity_type": "else", "pos": [129, 145]}], "task": "NER"}
{"text": "In May 2016 , NtechLab was admitted to the official testing of biometrics technology by NIST among the three Russian companies .", "entity": [{"entity": "NtechLab", "entity_type": "organization", "pos": [14, 22]}, {"entity": "biometrics", "entity_type": "field", "pos": [63, 73]}, {"entity": "NIST", "entity_type": "organization", "pos": [88, 92]}, {"entity": "Russian", "entity_type": "else", "pos": [109, 116]}], "task": "NER"}
{"text": "However , floating-point numbers have only a certain amount of mathematical precision .", "entity": [], "task": "NER"}
{"text": "During 2015 , many of SenseTime 's papers were accepted into the Conference on Computer Vision and Pattern Recognition ( CVPR ) .", "entity": [{"entity": "SenseTime", "entity_type": "organization", "pos": [22, 31]}, {"entity": "Conference on Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [65, 118]}, {"entity": "CVPR", "entity_type": "conference", "pos": [121, 125]}], "task": "NER"}
{"text": "He co-developed optimal algorithms for Structure From Motion ( SFM , or Visual SLAM , simultaneous localization and mapping , in Robotics ; Best Paper Award at Conference on Computer Vision and Pattern Recognition 1998 ) , characterized its ambiguities ( David Marr Prize at ICCV 1999 ) , also characterized the identifiability and observability of visual-inertial sensor fusion ( Best Paper Award at Robotics 2015 ) .", "entity": [{"entity": "Structure From Motion", "entity_type": "task", "pos": [39, 60]}, {"entity": "SFM", "entity_type": "task", "pos": [63, 66]}, {"entity": "Visual SLAM", "entity_type": "task", "pos": [72, 83]}, {"entity": "simultaneous localization and mapping", "entity_type": "task", "pos": [86, 123]}, {"entity": "Robotics", "entity_type": "field", "pos": [129, 137]}, {"entity": "Best Paper Award", "entity_type": "else", "pos": [140, 156]}, {"entity": "Conference on Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [160, 213]}, {"entity": "David Marr Prize", "entity_type": "else", "pos": [255, 271]}, {"entity": "ICCV 1999", "entity_type": "conference", "pos": [275, 284]}, {"entity": "Best Paper Award", "entity_type": "else", "pos": [381, 397]}, {"entity": "Robotics", "entity_type": "field", "pos": [401, 409]}], "task": "NER"}
{"text": "Stephen H. Muggleton FBCS , FIET , Association for the Advancement of Artificial Intelligence ,", "entity": [{"entity": "Stephen H. Muggleton", "entity_type": "researcher", "pos": [0, 20]}, {"entity": "FBCS", "entity_type": "organization", "pos": [21, 25]}, {"entity": "FIET", "entity_type": "organization", "pos": [28, 32]}, {"entity": "Association for the Advancement of Artificial Intelligence", "entity_type": "conference", "pos": [35, 93]}], "task": "NER"}
{"text": "Edge detection is a fundamental tool in image processing , machine vision and computer vision , particularly in the areas of feature detection and feature extraction .", "entity": [{"entity": "Edge detection", "entity_type": "task", "pos": [0, 14]}, {"entity": "image processing", "entity_type": "field", "pos": [40, 56]}, {"entity": "machine vision", "entity_type": "field", "pos": [59, 73]}, {"entity": "computer vision", "entity_type": "field", "pos": [78, 93]}, {"entity": "feature detection", "entity_type": "task", "pos": [125, 142]}, {"entity": "feature extraction", "entity_type": "task", "pos": [147, 165]}], "task": "NER"}
{"text": "An example of this would be a variable such as outside temperature ( mathtemp / math ) , which in a given application might be recorded to several decimal places of precision ( depending on the sensing apparatus ) .", "entity": [{"entity": "outside temperature", "entity_type": "else", "pos": [47, 66]}, {"entity": "decimal places of precision", "entity_type": "else", "pos": [147, 174]}], "task": "NER"}
{"text": "The returning judges are Fon Davis , Jessica Chobot , and Leland Melvin , as well as celebrity guest judges actor Clark Gregg , MythBusters host and former Battlebots builder Adam Savage , NFL tightend Vernon Davis , and YouTube star Michael Stevens a.k.a. Vsauce .", "entity": [{"entity": "Fon Davis", "entity_type": "person", "pos": [25, 34]}, {"entity": "Jessica Chobot", "entity_type": "person", "pos": [37, 51]}, {"entity": "Leland Melvin", "entity_type": "person", "pos": [58, 71]}, {"entity": "Clark Gregg", "entity_type": "person", "pos": [114, 125]}, {"entity": "MythBusters", "entity_type": "else", "pos": [128, 139]}, {"entity": "Battlebots", "entity_type": "else", "pos": [156, 166]}, {"entity": "Adam Savage", "entity_type": "person", "pos": [175, 186]}, {"entity": "NFL", "entity_type": "organization", "pos": [189, 192]}, {"entity": "Vernon Davis", "entity_type": "person", "pos": [202, 214]}, {"entity": "YouTube", "entity_type": "organization", "pos": [221, 228]}, {"entity": "Michael Stevens", "entity_type": "person", "pos": [234, 249]}, {"entity": "Vsauce", "entity_type": "person", "pos": [257, 263]}], "task": "NER"}
{"text": "But these methods never won over the non-uniform internal-handcrafting Gaussian mixture model / Hidden Markov model ( GMM-HMM ) technology based on generative models of speech trained discriminatively .", "entity": [{"entity": "Gaussian mixture model", "entity_type": "algorithm", "pos": [71, 93]}, {"entity": "Hidden Markov model", "entity_type": "algorithm", "pos": [96, 115]}, {"entity": "GMM-HMM", "entity_type": "algorithm", "pos": [118, 125]}], "task": "NER"}
{"text": "Software packages like MATLAB , GNU Octave , Scilab , and SciPy provide convenient ways to apply these different methods .", "entity": [{"entity": "MATLAB", "entity_type": "product", "pos": [23, 29]}, {"entity": "GNU Octave", "entity_type": "program language", "pos": [32, 42]}, {"entity": "Scilab", "entity_type": "program language", "pos": [45, 51]}, {"entity": "SciPy", "entity_type": "product", "pos": [58, 63]}], "task": "NER"}
{"text": "Linear predictive coding ( LPC ) , a speech processing algorithm , was first proposed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone ( NTT ) in 1966 .", "entity": [{"entity": "Linear predictive coding", "entity_type": "algorithm", "pos": [0, 24]}, {"entity": "LPC", "entity_type": "algorithm", "pos": [27, 30]}, {"entity": "speech processing", "entity_type": "task", "pos": [37, 54]}, {"entity": "Fumitada Itakura", "entity_type": "researcher", "pos": [89, 105]}, {"entity": "Nagoya University", "entity_type": "university", "pos": [109, 126]}, {"entity": "Shuzo Saito", "entity_type": "researcher", "pos": [131, 142]}, {"entity": "Nippon Telegraph and Telephone", "entity_type": "organization", "pos": [146, 176]}, {"entity": "NTT", "entity_type": "organization", "pos": [179, 182]}], "task": "NER"}
{"text": "In 2006 , for the 25th anniversary of the algorithm , a workshop was organized at the International Conference on Computer Vision and Pattern Recognition ( CVPR ) to summarize the most recent contributions and variations to the original algorithm , mostly meant to improve the speed of the algorithm , the robustness and accuracy of the estimated solution and to decrease the dependency from user defined constants .", "entity": [{"entity": "International Conference on Computer Vision and Pattern Recognition", "entity_type": "conference", "pos": [86, 153]}, {"entity": "CVPR", "entity_type": "conference", "pos": [156, 160]}], "task": "NER"}
{"text": "The members went to the University of Debrecen , the Hungarian Academy of Sciences , Eötvös Loránd University , etc .", "entity": [{"entity": "University of Debrecen", "entity_type": "university", "pos": [24, 46]}, {"entity": "Hungarian Academy of Sciences", "entity_type": "organization", "pos": [53, 82]}, {"entity": "Eötvös Loránd University", "entity_type": "university", "pos": [85, 109]}], "task": "NER"}
{"text": "To extend SVM to cases in which the data are not linearly separable , we introduce the loss function ,", "entity": [{"entity": "SVM", "entity_type": "algorithm", "pos": [10, 13]}, {"entity": "loss function", "entity_type": "else", "pos": [87, 100]}], "task": "NER"}
{"text": "Logo is an educational programming language , designed in 1967 by Wally Feurzeig , Seymour Papert , and Cynthia Solomon .", "entity": [{"entity": "Logo", "entity_type": "program language", "pos": [0, 4]}, {"entity": "Wally Feurzeig", "entity_type": "researcher", "pos": [66, 80]}, {"entity": "Seymour Papert", "entity_type": "researcher", "pos": [83, 97]}, {"entity": "Cynthia Solomon", "entity_type": "researcher", "pos": [104, 119]}], "task": "NER"}
{"text": "Eyring Research Institute was instrumental to the U.S. Air Force Missile Directorate at Hill Air Force Base near Ogden , Utah to produce in top military secrecy , the Intelligent Systems Technology Software that was foundational to the later named Reagan Star Wars program .", "entity": [{"entity": "Eyring Research Institute", "entity_type": "organization", "pos": [0, 25]}, {"entity": "U.S. Air Force Missile Directorate", "entity_type": "organization", "pos": [50, 84]}, {"entity": "Hill Air Force Base", "entity_type": "location", "pos": [88, 107]}, {"entity": "Ogden", "entity_type": "location", "pos": [113, 118]}, {"entity": "Utah", "entity_type": "location", "pos": [121, 125]}, {"entity": "Intelligent Systems Technology Software", "entity_type": "product", "pos": [167, 206]}, {"entity": "Reagan Star Wars program", "entity_type": "product", "pos": [248, 272]}], "task": "NER"}
{"text": "Over the decades he has researched and developed emerging fields of computer science from compiler , programming languages and system architecture John F. Sowa and John Zachman ( 1992 ) .", "entity": [{"entity": "computer science", "entity_type": "field", "pos": [68, 84]}, {"entity": "John F. Sowa", "entity_type": "researcher", "pos": [147, 159]}, {"entity": "John Zachman", "entity_type": "researcher", "pos": [164, 176]}], "task": "NER"}
{"text": "The Sobel operator , sometimes called the Sobel-Feldman operator or Sobel filter , is used in image processing and computer vision , particularly within edge detection algorithms where it creates an image emphasising edges .", "entity": [{"entity": "Sobel operator", "entity_type": "algorithm", "pos": [4, 18]}, {"entity": "Sobel-Feldman operator", "entity_type": "algorithm", "pos": [42, 64]}, {"entity": "Sobel filter", "entity_type": "algorithm", "pos": [68, 80]}, {"entity": "image processing", "entity_type": "field", "pos": [94, 110]}, {"entity": "computer vision", "entity_type": "field", "pos": [115, 130]}, {"entity": "edge detection algorithms", "entity_type": "else", "pos": [153, 178]}], "task": "NER"}
{"text": "LDA is a supervised learning algorithm that utilizes the labels of the data , while PCA is an learning algorithm that ignores the labels .", "entity": [{"entity": "LDA", "entity_type": "algorithm", "pos": [0, 3]}, {"entity": "supervised learning", "entity_type": "field", "pos": [9, 28]}, {"entity": "PCA", "entity_type": "algorithm", "pos": [84, 87]}], "task": "NER"}
{"text": "Other linear classification algorithms include Winnow , support vector machine and logistic regression .", "entity": [{"entity": "Winnow", "entity_type": "algorithm", "pos": [47, 53]}, {"entity": "support vector machine", "entity_type": "algorithm", "pos": [56, 78]}, {"entity": "logistic regression", "entity_type": "algorithm", "pos": [83, 102]}], "task": "NER"}
{"text": "VTK consists of a C + + class library and several interpreted interface layers including Tcl / Tk , Java , and Python .", "entity": [{"entity": "VTK", "entity_type": "product", "pos": [0, 3]}, {"entity": "C + +", "entity_type": "program language", "pos": [18, 23]}, {"entity": "Tcl / Tk", "entity_type": "product", "pos": [89, 97]}, {"entity": "Java", "entity_type": "program language", "pos": [100, 104]}, {"entity": "Python", "entity_type": "program language", "pos": [111, 117]}], "task": "NER"}
{"text": "Also , text produced by processing spontaneous speech using automatic speech recognition and printed or handwritten text using optical character recognition contains processing noise .", "entity": [{"entity": "automatic speech recognition", "entity_type": "task", "pos": [60, 88]}, {"entity": "optical character recognition", "entity_type": "task", "pos": [127, 156]}], "task": "NER"}
{"text": "Miller wrote several books and directed the development of WordNet , an online word-linkage database usable by computer programs .", "entity": [{"entity": "Miller", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "WordNet", "entity_type": "product", "pos": [59, 66]}], "task": "NER"}
{"text": "Contemporary automata are represented by the works of Cabaret Mechanical Theatre in the United Kingdom , Dug North and Chomick + Meder , Arthur Ganson , Joe Jones in the United States , Le Défenseur du Temps by French artist Jacques Monestier , and François Junod in Switzerland .", "entity": [{"entity": "automata", "entity_type": "field", "pos": [13, 21]}, {"entity": "Cabaret Mechanical Theatre", "entity_type": "organization", "pos": [54, 80]}, {"entity": "United Kingdom", "entity_type": "country", "pos": [88, 102]}, {"entity": "Dug North", "entity_type": "person", "pos": [105, 114]}, {"entity": "Chomick + Meder", "entity_type": "person", "pos": [119, 134]}, {"entity": "Arthur Ganson", "entity_type": "person", "pos": [137, 150]}, {"entity": "Joe Jones", "entity_type": "person", "pos": [153, 162]}, {"entity": "United States", "entity_type": "country", "pos": [170, 183]}, {"entity": "Le Défenseur du Temps", "entity_type": "location", "pos": [186, 207]}, {"entity": "French", "entity_type": "else", "pos": [211, 217]}, {"entity": "Jacques Monestier", "entity_type": "person", "pos": [225, 242]}, {"entity": "François Junod", "entity_type": "person", "pos": [249, 263]}, {"entity": "Switzerland", "entity_type": "country", "pos": [267, 278]}], "task": "NER"}
{"text": "MATLAB does include standard codefor / code and codewhile / code loops , but ( as in other similar applications such as R ) , using the vectorized notation is encouraged and is often faster to execute .", "entity": [{"entity": "MATLAB", "entity_type": "product", "pos": [0, 6]}, {"entity": "R", "entity_type": "program language", "pos": [120, 121]}], "task": "NER"}
{"text": "Pausch received two awards from Association for Computing Machinery in 2007 for his achievements in computing education : the Karl V. Karlstrom Outstanding Educator Award and the ACM SIGCSE Award for Outstanding Contributions to Computer Science Education .", "entity": [{"entity": "Pausch", "entity_type": "researcher", "pos": [0, 6]}, {"entity": "Association for Computing Machinery", "entity_type": "conference", "pos": [32, 67]}, {"entity": "computing education", "entity_type": "field", "pos": [100, 119]}, {"entity": "Karl V. Karlstrom Outstanding Educator Award", "entity_type": "else", "pos": [126, 170]}, {"entity": "ACM SIGCSE Award for Outstanding Contributions to Computer Science Education", "entity_type": "else", "pos": [179, 255]}], "task": "NER"}
{"text": "In 1960 , Devol personally sold the first Unimate robot , which was shipped in 1961 to General Motors .", "entity": [{"entity": "Devol", "entity_type": "person", "pos": [10, 15]}, {"entity": "Unimate", "entity_type": "product", "pos": [42, 49]}, {"entity": "robot", "entity_type": "product", "pos": [50, 55]}, {"entity": "General Motors", "entity_type": "organization", "pos": [87, 101]}], "task": "NER"}
{"text": "Semantic networks are used in natural language processing applications such as semantic parsing .", "entity": [{"entity": "Semantic networks", "entity_type": "algorithm", "pos": [0, 17]}, {"entity": "natural language processing", "entity_type": "field", "pos": [30, 57]}, {"entity": "semantic parsing", "entity_type": "task", "pos": [79, 95]}], "task": "NER"}
{"text": "Some successful applications of deep learning are computer vision and speech recognition . Honglak Lee , Roger Grosse , Rajesh Ranganath , Andrew Y. Ng .", "entity": [{"entity": "deep learning", "entity_type": "field", "pos": [32, 45]}, {"entity": "computer vision", "entity_type": "field", "pos": [50, 65]}, {"entity": "speech recognition", "entity_type": "task", "pos": [70, 88]}, {"entity": "Honglak Lee", "entity_type": "researcher", "pos": [91, 102]}, {"entity": "Roger Grosse", "entity_type": "researcher", "pos": [105, 117]}, {"entity": "Rajesh Ranganath", "entity_type": "researcher", "pos": [120, 136]}, {"entity": "Andrew Y. Ng", "entity_type": "researcher", "pos": [139, 151]}], "task": "NER"}
{"text": "In addition to maintaining the Discovery One spacecraft systems during the interplanetary mission to Jupiter ( or Saturn in the novel ) , HAL is capable of speech synthesis , speech recognition , facial recognition , natural language processing , lip reading , art appreciation , Affective computing , automated reasoning , spacecraft piloting and playing chess .", "entity": [{"entity": "Discovery One spacecraft systems", "entity_type": "product", "pos": [31, 63]}, {"entity": "Jupiter", "entity_type": "else", "pos": [101, 108]}, {"entity": "Saturn", "entity_type": "else", "pos": [114, 120]}, {"entity": "HAL", "entity_type": "product", "pos": [138, 141]}, {"entity": "speech synthesis", "entity_type": "task", "pos": [156, 172]}, {"entity": "speech recognition", "entity_type": "task", "pos": [175, 193]}, {"entity": "facial recognition", "entity_type": "task", "pos": [196, 214]}, {"entity": "natural language processing", "entity_type": "field", "pos": [217, 244]}, {"entity": "lip reading", "entity_type": "task", "pos": [247, 258]}, {"entity": "art appreciation", "entity_type": "field", "pos": [261, 277]}, {"entity": "Affective computing", "entity_type": "task", "pos": [280, 299]}, {"entity": "automated reasoning", "entity_type": "task", "pos": [302, 321]}, {"entity": "spacecraft piloting", "entity_type": "task", "pos": [324, 343]}, {"entity": "playing chess", "entity_type": "task", "pos": [348, 361]}], "task": "NER"}
{"text": "Dr. Julesz emigrated from Hungary to the United States following the 1956 Soviet invasion .", "entity": [{"entity": "Dr. Julesz", "entity_type": "researcher", "pos": [0, 10]}, {"entity": "Hungary", "entity_type": "country", "pos": [26, 33]}, {"entity": "the United States", "entity_type": "country", "pos": [37, 54]}, {"entity": "Soviet", "entity_type": "country", "pos": [74, 80]}], "task": "NER"}
{"text": "Sigmoid function activation functions use a second non-linearity for large inputs : math \\ phi ( v _ i ) = ( 1 + \\ exp ( -v _ i ) ) ^ { -1 } / math .", "entity": [{"entity": "Sigmoid function", "entity_type": "algorithm", "pos": [0, 16]}], "task": "NER"}
{"text": "These probabilities are used to determine what the target is using a maximum likelihood decision .", "entity": [{"entity": "maximum likelihood decision", "entity_type": "algorithm", "pos": [69, 96]}], "task": "NER"}
{"text": "In 1984 he moved to the University of Konstanz and in 1990 to the University of Salzburg .", "entity": [{"entity": "University of Konstanz", "entity_type": "university", "pos": [24, 46]}, {"entity": "University of Salzburg", "entity_type": "university", "pos": [66, 88]}], "task": "NER"}
{"text": "Some popular fitness functions based on the confusion matrix include sensitivity / specificity , recall / precision , F-measure , Jaccard similarity , Matthews correlation coefficient , and cost / gain matrix which combines the costs and gains assigned to the 4 different types of classifications .", "entity": [{"entity": "confusion matrix", "entity_type": "metrics", "pos": [44, 60]}, {"entity": "sensitivity / specificity", "entity_type": "metrics", "pos": [69, 94]}, {"entity": "recall / precision", "entity_type": "metrics", "pos": [97, 115]}, {"entity": "F-measure", "entity_type": "metrics", "pos": [118, 127]}, {"entity": "Jaccard similarity", "entity_type": "metrics", "pos": [130, 148]}, {"entity": "Matthews correlation coefficient", "entity_type": "metrics", "pos": [151, 183]}, {"entity": "cost / gain matrix", "entity_type": "metrics", "pos": [190, 208]}], "task": "NER"}
{"text": "Common numerical programming environments such as MATLAB , SciLab , NumPy , Sklearn and the R language provide some of the simpler feature extraction techniques ( e.g. principal component analysis ) via built-in commands .", "entity": [{"entity": "MATLAB", "entity_type": "product", "pos": [50, 56]}, {"entity": "SciLab", "entity_type": "product", "pos": [59, 65]}, {"entity": "NumPy", "entity_type": "product", "pos": [68, 73]}, {"entity": "Sklearn", "entity_type": "product", "pos": [76, 83]}, {"entity": "R language", "entity_type": "program language", "pos": [92, 102]}, {"entity": "principal component analysis", "entity_type": "algorithm", "pos": [168, 196]}], "task": "NER"}
{"text": "Industrial robots have been implemented to collaborate with humans to perform industrial manufacturing tasks .", "entity": [{"entity": "Industrial robots", "entity_type": "product", "pos": [0, 17]}], "task": "NER"}
{"text": "In the first published paper on CGs , John F. Sowa applied them to a wide range of topics in artificial intelligence , computer science , and cognitive science .", "entity": [{"entity": "CGs", "entity_type": "field", "pos": [32, 35]}, {"entity": "John F. Sowa", "entity_type": "researcher", "pos": [38, 50]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [93, 116]}, {"entity": "computer science", "entity_type": "field", "pos": [119, 135]}, {"entity": "cognitive science", "entity_type": "field", "pos": [142, 159]}], "task": "NER"}
{"text": "NIST also differs from BLEU in its calculation of the brevity penalty , insofar as small variations in translation length do not impact the overall score as much .", "entity": [{"entity": "NIST", "entity_type": "metrics", "pos": [0, 4]}, {"entity": "BLEU", "entity_type": "metrics", "pos": [23, 27]}, {"entity": "brevity penalty", "entity_type": "else", "pos": [54, 69]}], "task": "NER"}
{"text": "The IJCAI Award for Research Excellence is a biannual award given at the IJCAI conference to researcher in artificial intelligence as a recognition of excellence of their career .", "entity": [{"entity": "IJCAI Award for Research Excellence", "entity_type": "else", "pos": [4, 39]}, {"entity": "IJCAI", "entity_type": "conference", "pos": [73, 78]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [107, 130]}], "task": "NER"}
{"text": "Lenat was one of the original Fellows of the AAAI , and is the only individual to have on the Scientific Advisory Boards of both Microsoft and Apple .", "entity": [{"entity": "Lenat", "entity_type": "researcher", "pos": [0, 5]}, {"entity": "AAAI", "entity_type": "conference", "pos": [45, 49]}, {"entity": "Scientific Advisory Boards of both Microsoft and Apple", "entity_type": "organization", "pos": [94, 148]}], "task": "NER"}
{"text": "Autoencoders are trained to minimise reconstruction errors ( such as Mean squared error ) , often referred to as the loss :", "entity": [{"entity": "Autoencoders", "entity_type": "algorithm", "pos": [0, 12]}, {"entity": "Mean squared error", "entity_type": "metrics", "pos": [69, 87]}, {"entity": "loss", "entity_type": "else", "pos": [117, 121]}], "task": "NER"}
{"text": "An alternative to the use of the definitions is to consider general word-sense relatedness and to compute the similarity of each pair of word senses based on a given lexical knowledge base such as WordNet .", "entity": [{"entity": "lexical knowledge base", "entity_type": "else", "pos": [166, 188]}, {"entity": "WordNet", "entity_type": "product", "pos": [197, 204]}], "task": "NER"}
{"text": "TD-Lambda is a learning algorithm invented by Richard S. Sutton based on earlier work on temporal difference learning by Arthur Samuel .", "entity": [{"entity": "TD-Lambda", "entity_type": "algorithm", "pos": [0, 9]}, {"entity": "Richard S. Sutton", "entity_type": "researcher", "pos": [46, 63]}, {"entity": "Arthur Samuel", "entity_type": "researcher", "pos": [121, 134]}], "task": "NER"}
{"text": "In data mining and statistics , hierarchical clustering ( also called hierarchical cluster analysis or HCA ) is a method of cluster analysis which seeks to build a hierarchy of clusters .", "entity": [{"entity": "data mining", "entity_type": "field", "pos": [3, 14]}, {"entity": "statistics", "entity_type": "field", "pos": [19, 29]}, {"entity": "hierarchical clustering", "entity_type": "task", "pos": [32, 55]}, {"entity": "hierarchical cluster analysis", "entity_type": "task", "pos": [70, 99]}, {"entity": "HCA", "entity_type": "task", "pos": [103, 106]}, {"entity": "cluster analysis", "entity_type": "task", "pos": [124, 140]}], "task": "NER"}
{"text": "The concept of deconvolution is widely used in the techniques of signal processing and image processing .", "entity": [{"entity": "deconvolution", "entity_type": "algorithm", "pos": [15, 28]}, {"entity": "signal processing", "entity_type": "field", "pos": [65, 82]}, {"entity": "image processing", "entity_type": "field", "pos": [87, 103]}], "task": "NER"}
{"text": "Cognitive maps serve the construction and accumulation of spatial knowledge , allowing the mind 's eye to visualize images in order to reduce cognitive load , enhance recall and learning of information .", "entity": [{"entity": "Cognitive maps", "entity_type": "algorithm", "pos": [0, 14]}, {"entity": "cognitive load", "entity_type": "else", "pos": [142, 156]}, {"entity": "recall", "entity_type": "metrics", "pos": [167, 173]}], "task": "NER"}
{"text": ", typically providing bindings to languages such as Python , C + + , Java ) .", "entity": [{"entity": "Python", "entity_type": "program language", "pos": [52, 58]}, {"entity": "C + +", "entity_type": "program language", "pos": [61, 66]}, {"entity": "Java", "entity_type": "program language", "pos": [69, 73]}], "task": "NER"}
{"text": "A voice-user interface ( VUI ) makes spoken human interaction with computers possible , using speech recognition to understand spoken commands and Question answering , and typically text to speech to play a reply .", "entity": [{"entity": "voice-user interface", "entity_type": "product", "pos": [2, 22]}, {"entity": "VUI", "entity_type": "product", "pos": [25, 28]}, {"entity": "speech recognition", "entity_type": "task", "pos": [94, 112]}, {"entity": "Question answering", "entity_type": "task", "pos": [147, 165]}, {"entity": "text to speech", "entity_type": "task", "pos": [182, 196]}], "task": "NER"}
{"text": "Jess is a rule engine for the Java platform that was developed by Ernest Friedman-Hill of Sandia National .", "entity": [{"entity": "Jess", "entity_type": "program language", "pos": [0, 4]}, {"entity": "rule engine", "entity_type": "else", "pos": [10, 21]}, {"entity": "Java", "entity_type": "program language", "pos": [30, 34]}, {"entity": "Ernest Friedman-Hill", "entity_type": "researcher", "pos": [66, 86]}, {"entity": "Sandia National", "entity_type": "organization", "pos": [90, 105]}], "task": "NER"}
{"text": "For multilayer perceptron s , where a hidden layer exists , more sophisticated algorithms such as backpropagation must be used .", "entity": [{"entity": "multilayer perceptron", "entity_type": "algorithm", "pos": [4, 25]}, {"entity": "backpropagation", "entity_type": "algorithm", "pos": [98, 113]}], "task": "NER"}
{"text": "Google Translate 's neural machine translation system uses a large end-to-end artificial neural network that attempts to perform deep learning , in particular , long short-term memory networks .", "entity": [{"entity": "Google Translate", "entity_type": "product", "pos": [0, 16]}, {"entity": "neural machine translation system", "entity_type": "product", "pos": [20, 53]}, {"entity": "end-to-end artificial neural network", "entity_type": "algorithm", "pos": [67, 103]}, {"entity": "deep learning", "entity_type": "field", "pos": [129, 142]}, {"entity": "long short-term memory networks", "entity_type": "algorithm", "pos": [161, 192]}], "task": "NER"}
{"text": "Various methods for doing so were developed in the 1980s and early 1990s by Werbos , Williams , Robinson , Jürgen Schmidhuber , Sepp Hochreiter , Pearlmutter and others .", "entity": [{"entity": "Werbos", "entity_type": "researcher", "pos": [76, 82]}, {"entity": "Williams", "entity_type": "researcher", "pos": [85, 93]}, {"entity": "Robinson", "entity_type": "researcher", "pos": [96, 104]}, {"entity": "Jürgen Schmidhuber", "entity_type": "researcher", "pos": [107, 125]}, {"entity": "Sepp Hochreiter", "entity_type": "researcher", "pos": [128, 143]}, {"entity": "Pearlmutter", "entity_type": "researcher", "pos": [146, 157]}], "task": "NER"}
{"text": "| Apple Apple Inc originally licensed software from Nuance to provide speech recognition capability to its digital assistant Siri .", "entity": [{"entity": "Apple", "entity_type": "organization", "pos": [2, 7]}, {"entity": "Apple Inc", "entity_type": "organization", "pos": [8, 17]}, {"entity": "Nuance", "entity_type": "organization", "pos": [52, 58]}, {"entity": "speech recognition", "entity_type": "task", "pos": [70, 88]}, {"entity": "Siri", "entity_type": "product", "pos": [125, 129]}], "task": "NER"}
{"text": "Columbia released several 3D westerns produced by Sam Katzman and directed by William Castle .", "entity": [{"entity": "Columbia", "entity_type": "organization", "pos": [0, 8]}, {"entity": "3D westerns", "entity_type": "else", "pos": [26, 37]}, {"entity": "Sam Katzman", "entity_type": "person", "pos": [50, 61]}, {"entity": "William Castle", "entity_type": "person", "pos": [78, 92]}], "task": "NER"}
{"text": "It incorporates knowledge and research in the computer science , linguistics and computer engineering fields .", "entity": [{"entity": "computer science", "entity_type": "field", "pos": [46, 62]}, {"entity": "linguistics", "entity_type": "field", "pos": [65, 76]}, {"entity": "computer engineering", "entity_type": "field", "pos": [81, 101]}], "task": "NER"}
{"text": "Here is an example of R code :", "entity": [{"entity": "R", "entity_type": "program language", "pos": [22, 23]}], "task": "NER"}
{"text": "The ROC curve is created by plotting the TRUE positive rate ( TPR ) against the FALSE positive rate ( FPR ) at various threshold settings .", "entity": [{"entity": "ROC curve", "entity_type": "metrics", "pos": [4, 13]}, {"entity": "TRUE positive rate", "entity_type": "metrics", "pos": [41, 59]}, {"entity": "TPR", "entity_type": "metrics", "pos": [62, 65]}, {"entity": "FALSE positive rate", "entity_type": "metrics", "pos": [80, 99]}, {"entity": "FPR", "entity_type": "metrics", "pos": [102, 105]}], "task": "NER"}
{"text": "Research stagnated after machine learning research by Marvin Minsky and Seymour Papert ( 1969 ) ,", "entity": [{"entity": "machine learning", "entity_type": "field", "pos": [25, 41]}, {"entity": "Marvin Minsky", "entity_type": "researcher", "pos": [54, 67]}, {"entity": "Seymour Papert", "entity_type": "researcher", "pos": [72, 86]}], "task": "NER"}
{"text": "Other programming environments that are used to build DAQ applications include ladder logic , Visual C + + , Visual Basic , LabVIEW , and MATLAB .", "entity": [{"entity": "DAQ", "entity_type": "task", "pos": [54, 57]}, {"entity": "ladder logic", "entity_type": "program language", "pos": [79, 91]}, {"entity": "Visual C + +", "entity_type": "product", "pos": [94, 106]}, {"entity": "Visual Basic", "entity_type": "program language", "pos": [109, 121]}, {"entity": "LabVIEW", "entity_type": "product", "pos": [124, 131]}, {"entity": "MATLAB", "entity_type": "product", "pos": [138, 144]}], "task": "NER"}
{"text": "The metric was designed to fix some of the problems found in the more popular BLEU metric , and also produce good correlation with human judgement at the sentence or segment level .", "entity": [{"entity": "BLEU metric", "entity_type": "metrics", "pos": [78, 89]}], "task": "NER"}
{"text": "Techniques such as dynamic Markov Networks , Convolutional neural network and Long short-term memory are often employed to exploit the semantic correlations between consecutive video frames .", "entity": [{"entity": "dynamic Markov Networks", "entity_type": "algorithm", "pos": [19, 42]}, {"entity": "Convolutional neural network", "entity_type": "algorithm", "pos": [45, 73]}, {"entity": "Long short-term memory", "entity_type": "algorithm", "pos": [78, 100]}], "task": "NER"}
{"text": "Mass-produced printed circuit board s ( PCBs ) are almost exclusively manufactured by pick-and-place robots , typically with SCARA manipulators , which remove tiny electronic component s from strips or trays , and place them on to PCBs with great accuracy .", "entity": [{"entity": "printed circuit board", "entity_type": "product", "pos": [14, 35]}, {"entity": "PCBs", "entity_type": "product", "pos": [40, 44]}, {"entity": "pick-and-place robots", "entity_type": "product", "pos": [86, 107]}, {"entity": "SCARA", "entity_type": "product", "pos": [125, 130]}, {"entity": "PCBs", "entity_type": "product", "pos": [231, 235]}], "task": "NER"}
{"text": "In the context of machine learning , where it is most widely applied today , LDA was rediscovered independently by David Blei , Andrew Ng and Michael I. Jordan in 2003 , and presented as a graphical model for topic discovery .", "entity": [{"entity": "machine learning", "entity_type": "field", "pos": [18, 34]}, {"entity": "LDA", "entity_type": "algorithm", "pos": [77, 80]}, {"entity": "David Blei", "entity_type": "researcher", "pos": [115, 125]}, {"entity": "Andrew Ng", "entity_type": "researcher", "pos": [128, 137]}, {"entity": "Michael I. Jordan", "entity_type": "researcher", "pos": [142, 159]}, {"entity": "graphical model", "entity_type": "algorithm", "pos": [189, 204]}, {"entity": "topic discovery", "entity_type": "task", "pos": [209, 224]}], "task": "NER"}
{"text": "The measured performance on test data of eight naive WSI across various tauopathies resulted in the recall , precision , and an F1 score of 0.92 , 0.72 , and 0.81 , respectively .", "entity": [{"entity": "WSI", "entity_type": "task", "pos": [53, 56]}, {"entity": "tauopathies", "entity_type": "else", "pos": [72, 83]}, {"entity": "recall", "entity_type": "metrics", "pos": [100, 106]}, {"entity": "precision", "entity_type": "metrics", "pos": [109, 118]}, {"entity": "F1 score", "entity_type": "metrics", "pos": [128, 136]}], "task": "NER"}
{"text": "With the help of advanced AR technologies ( e.g. adding computer vision , incorporating AR cameras into smartphone and object recognition ) the information about the surrounding real world of the user becomes interactive and digitally manipulated .", "entity": [{"entity": "AR", "entity_type": "field", "pos": [26, 28]}, {"entity": "computer vision", "entity_type": "field", "pos": [56, 71]}, {"entity": "AR", "entity_type": "field", "pos": [88, 90]}, {"entity": "object recognition", "entity_type": "task", "pos": [119, 137]}], "task": "NER"}
{"text": "In 2014 , Schmidhuber formed a company , Nnaisense , to work on commercial applications of artificial intelligence in fields such as finance , heavy industry and self-driving car s .", "entity": [{"entity": "Schmidhuber", "entity_type": "researcher", "pos": [10, 21]}, {"entity": "Nnaisense", "entity_type": "organization", "pos": [41, 50]}, {"entity": "artificial intelligence", "entity_type": "field", "pos": [91, 114]}, {"entity": "self-driving car", "entity_type": "product", "pos": [162, 178]}], "task": "NER"}
{"text": "Not only does this alter the performance of all subsequent tests on the retained explanatory model , it may introduce bias and alter mean square error in estimation .", "entity": [{"entity": "mean square error", "entity_type": "metrics", "pos": [133, 150]}], "task": "NER"}
{"text": "Bigrams are used in most successful language model s for speech recognition .", "entity": [{"entity": "Bigrams", "entity_type": "else", "pos": [0, 7]}, {"entity": "language model", "entity_type": "algorithm", "pos": [36, 50]}, {"entity": "speech recognition", "entity_type": "task", "pos": [57, 75]}], "task": "NER"}
{"text": "His research in cognitive psychology has won the Early Career Award ( 1984 ) and Boyd McCandless Award 1986 ) from the American Psychological Association , the Troland Research Award ( 1993 ) from the National Academy of Sciences , the Henry Dale Prize ( 2004 ) from the Royal Institution of Great Britain , and the George Miller Prize ( 2010 ) from the Cognitive Neuroscience Society .", "entity": [{"entity": "cognitive psychology", "entity_type": "field", "pos": [16, 36]}, {"entity": "Early Career Award", "entity_type": "else", "pos": [49, 67]}, {"entity": "Boyd McCandless Award", "entity_type": "else", "pos": [81, 102]}, {"entity": "American Psychological Association", "entity_type": "organization", "pos": [119, 153]}, {"entity": "Troland Research Award", "entity_type": "else", "pos": [160, 182]}, {"entity": "National Academy of Sciences", "entity_type": "organization", "pos": [201, 229]}, {"entity": "Henry Dale Prize", "entity_type": "else", "pos": [236, 252]}, {"entity": "Royal Institution of Great Britain", "entity_type": "organization", "pos": [271, 305]}, {"entity": "George Miller Prize", "entity_type": "else", "pos": [316, 335]}, {"entity": "Cognitive Neuroscience Society", "entity_type": "organization", "pos": [354, 384]}], "task": "NER"}
{"text": "An eigenface ( The approach of using eigenfaces for Facial recognition system was developed by Sirovich and Kirby ( 1987 ) and used by Matthew Turk and Alex Pentland in face classification . Turk , Matthew A and Pentland , Alex P. Face recognition using eigenfaces .", "entity": [{"entity": "eigenface", "entity_type": "else", "pos": [3, 12]}, {"entity": "eigenfaces", "entity_type": "else", "pos": [37, 47]}, {"entity": "Facial recognition system", "entity_type": "product", "pos": [52, 77]}, {"entity": "Sirovich", "entity_type": "researcher", "pos": [95, 103]}, {"entity": "Kirby", "entity_type": "researcher", "pos": [108, 113]}, {"entity": "Matthew Turk", "entity_type": "researcher", "pos": [135, 147]}, {"entity": "Alex Pentland", "entity_type": "researcher", "pos": [152, 165]}, {"entity": "face classification", "entity_type": "task", "pos": [169, 188]}, {"entity": "Turk , Matthew A", "entity_type": "researcher", "pos": [191, 207]}, {"entity": "Pentland , Alex P.", "entity_type": "researcher", "pos": [212, 230]}, {"entity": "Face recognition", "entity_type": "task", "pos": [231, 247]}, {"entity": "eigenfaces", "entity_type": "else", "pos": [254, 264]}], "task": "NER"}
{"text": "A lexical dictionary such as WordNet can then be used for understanding the context .", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [29, 36]}], "task": "NER"}
{"text": "Hyponymy is the most frequently encoded relation among synsets used in lexical databases such as WordNet .", "entity": [{"entity": "Hyponymy", "entity_type": "else", "pos": [0, 8]}, {"entity": "synsets", "entity_type": "else", "pos": [55, 62]}, {"entity": "WordNet", "entity_type": "product", "pos": [97, 104]}], "task": "NER"}
{"text": "OPeNDAP offers open-source libraries in C + + and Java , but many clients rely on community developed libraries such as libraries include embedded capabilities for retrieving ( array-style ) data from DAP servers .", "entity": [{"entity": "OPeNDAP", "entity_type": "organization", "pos": [0, 7]}, {"entity": "C + +", "entity_type": "program language", "pos": [40, 45]}, {"entity": "Java", "entity_type": "program language", "pos": [50, 54]}, {"entity": "DAP", "entity_type": "else", "pos": [201, 204]}], "task": "NER"}
{"text": "In that page , Samurai Damashii exaggerated the Senkousha as the crystallization of China 's four thousand years of scientific knowledge , commented on the crude design ( e.g. the Chinese Cannon on its crotch ) , and put its image among images of Honda ' s ASIMO and Sony ' s QRIO SDR-3X for juxtaposition .", "entity": [{"entity": "Samurai Damashii", "entity_type": "else", "pos": [15, 31]}, {"entity": "Senkousha", "entity_type": "product", "pos": [48, 57]}, {"entity": "China", "entity_type": "country", "pos": [84, 89]}, {"entity": "Chinese Cannon", "entity_type": "else", "pos": [180, 194]}, {"entity": "Honda", "entity_type": "organization", "pos": [247, 252]}, {"entity": "ASIMO", "entity_type": "product", "pos": [257, 262]}, {"entity": "Sony", "entity_type": "organization", "pos": [267, 271]}, {"entity": "QRIO SDR-3X", "entity_type": "product", "pos": [276, 287]}], "task": "NER"}
{"text": "There are also many programming libraries that contain neural network functionality and that can be used in custom implementations ( such as TensorFlow , Theano , etc .", "entity": [{"entity": "neural network", "entity_type": "algorithm", "pos": [55, 69]}, {"entity": "TensorFlow", "entity_type": "product", "pos": [141, 151]}, {"entity": "Theano", "entity_type": "product", "pos": [154, 160]}], "task": "NER"}
{"text": "He is a Fellow of the Association for Computing Machinery , IEEE , American Association for the Advancement of Science , IAPR and SPIE .", "entity": [{"entity": "Association for Computing Machinery", "entity_type": "conference", "pos": [22, 57]}, {"entity": "IEEE", "entity_type": "organization", "pos": [60, 64]}, {"entity": "American Association for the Advancement of Science", "entity_type": "conference", "pos": [67, 118]}, {"entity": "IAPR", "entity_type": "conference", "pos": [121, 125]}, {"entity": "SPIE", "entity_type": "conference", "pos": [130, 134]}], "task": "NER"}
{"text": "A trial by RET in 2011 with Facial recognition system cameras mounted on trams made sure that people were banned from the city trams did not sneak on anyway .", "entity": [{"entity": "RET", "entity_type": "organization", "pos": [11, 14]}, {"entity": "Facial recognition system", "entity_type": "product", "pos": [28, 53]}], "task": "NER"}
{"text": "The film , adapted from the popular Cole Porter Broadway musical , starred the MGM songbird team of Howard Keel and Kathryn Grayson as the leads , supported by Ann Miller , Keenan Wynn , Bobby Van , James Whitmore , Kurt Kasznar and Tommy Rall .", "entity": [{"entity": "Cole Porter", "entity_type": "person", "pos": [36, 47]}, {"entity": "Broadway", "entity_type": "organization", "pos": [48, 56]}, {"entity": "Howard Keel", "entity_type": "person", "pos": [100, 111]}, {"entity": "Kathryn Grayson", "entity_type": "person", "pos": [116, 131]}, {"entity": "Ann Miller", "entity_type": "person", "pos": [160, 170]}, {"entity": "Keenan Wynn", "entity_type": "person", "pos": [173, 184]}, {"entity": "Bobby Van", "entity_type": "person", "pos": [187, 196]}, {"entity": "James Whitmore", "entity_type": "person", "pos": [199, 213]}, {"entity": "Kurt Kasznar", "entity_type": "person", "pos": [216, 228]}, {"entity": "Tommy Rall", "entity_type": "person", "pos": [233, 243]}], "task": "NER"}
{"text": "Such applications should streamline the call flows , minimize prompts , eliminate unnecessary iterations and allow elaborate mixed initiative dialog system , which enable callers to enter several pieces of information in a single utterance and in any order or combination .", "entity": [{"entity": "mixed initiative dialog system", "entity_type": "product", "pos": [125, 155]}], "task": "NER"}
{"text": "As such , traditional gradient descent ( or Stochastic gradient descent ) methods can be adapted , where of taking a step in the direction of the function 's gradient , a step is taken in the direction of a vector selected from the function 's sub-gradient .", "entity": [{"entity": "gradient descent", "entity_type": "algorithm", "pos": [22, 38]}, {"entity": "Stochastic gradient descent", "entity_type": "algorithm", "pos": [44, 71]}], "task": "NER"}
{"text": "If it is assumed that distortion is measured by mean squared error , the distortion D , is given by :", "entity": [{"entity": "mean squared error", "entity_type": "metrics", "pos": [48, 66]}, {"entity": "distortion D", "entity_type": "else", "pos": [73, 85]}], "task": "NER"}
{"text": "MLPs were a popular machine learning solution in the 1980s , finding applications in diverse fields such as speech recognition , image recognition , and machine translation software , Neural networks .", "entity": [{"entity": "MLPs", "entity_type": "algorithm", "pos": [0, 4]}, {"entity": "machine learning", "entity_type": "field", "pos": [20, 36]}, {"entity": "speech recognition", "entity_type": "task", "pos": [108, 126]}, {"entity": "image recognition", "entity_type": "task", "pos": [129, 146]}, {"entity": "machine translation", "entity_type": "task", "pos": [153, 172]}, {"entity": "Neural networks", "entity_type": "product", "pos": [184, 199]}], "task": "NER"}
{"text": "Allen received his Ph.D. from the University of Toronto in 1979 , under the supervision of C. Raymond Perrault ,", "entity": [{"entity": "Allen", "entity_type": "researcher", "pos": [0, 5]}, {"entity": "University of Toronto", "entity_type": "university", "pos": [34, 55]}, {"entity": "C. Raymond Perrault", "entity_type": "researcher", "pos": [91, 110]}], "task": "NER"}
{"text": "OpenCV supports some models from deep learning frameworks like TensorFlow , Torch , PyTorch ( after converting to an ONNX model ) and Caffe according to a defined list of supported layers .", "entity": [{"entity": "OpenCV", "entity_type": "product", "pos": [0, 6]}, {"entity": "deep learning", "entity_type": "field", "pos": [33, 46]}, {"entity": "TensorFlow", "entity_type": "product", "pos": [63, 73]}, {"entity": "Torch", "entity_type": "product", "pos": [76, 81]}, {"entity": "PyTorch", "entity_type": "product", "pos": [84, 91]}, {"entity": "ONNX", "entity_type": "product", "pos": [117, 121]}, {"entity": "Caffe", "entity_type": "product", "pos": [134, 139]}], "task": "NER"}
{"text": "Previously , Christensen was the Founding Chairman of European Robotics Research Network ( EURON ) and an IEEE Robotics and Automation Society Distinguished Lecturer in Robotics .", "entity": [{"entity": "Christensen", "entity_type": "researcher", "pos": [13, 24]}, {"entity": "European Robotics Research Network", "entity_type": "organization", "pos": [54, 88]}, {"entity": "EURON", "entity_type": "organization", "pos": [91, 96]}, {"entity": "IEEE Robotics and Automation Society", "entity_type": "organization", "pos": [106, 142]}, {"entity": "Robotics", "entity_type": "field", "pos": [169, 177]}], "task": "NER"}
{"text": "He received his master 's degree in mathematics from the Samarkand State University , Samarkand , Uzbek Soviet Socialist Republic in 1958 and Ph.D in statistics at the Institute of Control Sciences , Moscow in 1964 .", "entity": [{"entity": "mathematics", "entity_type": "field", "pos": [36, 47]}, {"entity": "Samarkand State University", "entity_type": "university", "pos": [57, 83]}, {"entity": "Samarkand", "entity_type": "location", "pos": [86, 95]}, {"entity": "Uzbek Soviet Socialist Republic", "entity_type": "country", "pos": [98, 129]}, {"entity": "Ph.D", "entity_type": "else", "pos": [142, 146]}, {"entity": "statistics", "entity_type": "field", "pos": [150, 160]}, {"entity": "Institute of Control Sciences", "entity_type": "organization", "pos": [168, 197]}, {"entity": "Moscow", "entity_type": "location", "pos": [200, 206]}], "task": "NER"}
{"text": "Increasingly , however , work at Cycorp involves giving the Cyc system the ability to communicate with end users in natural language , and to assist with the ongoing knowledge formation process via machine learning and natural language understanding .", "entity": [{"entity": "Cycorp", "entity_type": "organization", "pos": [33, 39]}, {"entity": "Cyc system", "entity_type": "product", "pos": [60, 70]}, {"entity": "machine learning", "entity_type": "field", "pos": [198, 214]}, {"entity": "natural language understanding", "entity_type": "task", "pos": [219, 249]}], "task": "NER"}
{"text": "For example , if the most suitable classifier for the problem is sought , the training dataset is used to train the candidate algorithms , the validation dataset is used to compare their performances and decide which one to take and , finally , the test dataset is used to obtain the performance characteristics such as accuracy , sensitivity , specificity , F-measure , and so on .", "entity": [{"entity": "accuracy", "entity_type": "metrics", "pos": [320, 328]}, {"entity": "sensitivity", "entity_type": "metrics", "pos": [331, 342]}, {"entity": "specificity", "entity_type": "metrics", "pos": [345, 356]}, {"entity": "F-measure", "entity_type": "metrics", "pos": [359, 368]}], "task": "NER"}
{"text": "The Mean squared error is 0.15 .", "entity": [{"entity": "Mean squared error", "entity_type": "metrics", "pos": [4, 22]}], "task": "NER"}
{"text": "In 1979 a Micromouse competition was organized by the IEEE as shown in the Spectrum magazine .", "entity": [{"entity": "Micromouse competition", "entity_type": "else", "pos": [10, 32]}, {"entity": "IEEE", "entity_type": "organization", "pos": [54, 58]}, {"entity": "Spectrum", "entity_type": "else", "pos": [75, 83]}], "task": "NER"}
{"text": "The Gabor space is very useful in image processing applications such as optical character recognition , iris recognition and fingerprint recognition .", "entity": [{"entity": "Gabor space", "entity_type": "algorithm", "pos": [4, 15]}, {"entity": "image processing", "entity_type": "field", "pos": [34, 50]}, {"entity": "optical character recognition", "entity_type": "task", "pos": [72, 101]}, {"entity": "iris recognition", "entity_type": "task", "pos": [104, 120]}, {"entity": "fingerprint recognition", "entity_type": "task", "pos": [125, 148]}], "task": "NER"}
{"text": "or via high-level interfaces to Java and Tcl .", "entity": [{"entity": "Java", "entity_type": "program language", "pos": [32, 36]}, {"entity": "Tcl", "entity_type": "program language", "pos": [41, 44]}], "task": "NER"}
{"text": "In recent research , kernel-based methods such as support vector machine s have shown superior performance in supervised .", "entity": [{"entity": "support vector machine", "entity_type": "algorithm", "pos": [50, 72]}, {"entity": "supervised", "entity_type": "field", "pos": [110, 120]}], "task": "NER"}
{"text": "To illustrate the basic principles of bagging , below is an analysis on the relationship between ozone and temperature ( data from Rousseeuw and Leroy ( 1986 ) , analysis done in R ) .", "entity": [{"entity": "ozone", "entity_type": "else", "pos": [97, 102]}, {"entity": "Rousseeuw", "entity_type": "researcher", "pos": [131, 140]}, {"entity": "Leroy", "entity_type": "researcher", "pos": [145, 150]}, {"entity": "R", "entity_type": "program language", "pos": [179, 180]}], "task": "NER"}
{"text": "Denso Wave is a subsidiary that produces automatic identification products ( bar-code reader s and related products ) , industrial robot s and programmable logic controller s .", "entity": [{"entity": "Denso Wave", "entity_type": "organization", "pos": [0, 10]}, {"entity": "bar-code reader", "entity_type": "product", "pos": [77, 92]}, {"entity": "industrial robot", "entity_type": "product", "pos": [120, 136]}, {"entity": "programmable logic controller", "entity_type": "product", "pos": [143, 172]}], "task": "NER"}
{"text": "Where Bilingual evaluation understudy simply calculates n-gram precision adding equal weight to each one , NIST also calculates how informative a particular n-gram is .", "entity": [{"entity": "Bilingual evaluation understudy", "entity_type": "metrics", "pos": [6, 37]}, {"entity": "n-gram precision", "entity_type": "metrics", "pos": [56, 72]}, {"entity": "NIST", "entity_type": "metrics", "pos": [107, 111]}, {"entity": "n-gram", "entity_type": "else", "pos": [157, 163]}], "task": "NER"}
{"text": "In particular , they are used during the calculation of likelihood of a tree ( in Bayesian and maximum likelihood approaches to tree estimation ) and they are used to estimate the evolutionary distance between sequences from the observed differences between the sequences .", "entity": [{"entity": "Bayesian", "entity_type": "algorithm", "pos": [82, 90]}, {"entity": "maximum likelihood", "entity_type": "algorithm", "pos": [95, 113]}], "task": "NER"}
{"text": "The Audio Engineering Society recommends 48 kHz sampling rate for most applications but gives recognition to 44.1 kHz for Compact Disc ( CD ) and other consumer uses , 32 kHz for transmission-related applications , and 96 kHz for higher bandwidth or relaxed anti-aliasing filter ing .", "entity": [{"entity": "Audio Engineering Society", "entity_type": "conference", "pos": [4, 29]}, {"entity": "Compact Disc", "entity_type": "else", "pos": [122, 134]}, {"entity": "CD", "entity_type": "else", "pos": [137, 139]}, {"entity": "anti-aliasing filter", "entity_type": "else", "pos": [258, 278]}], "task": "NER"}
{"text": "Resources for affectivity of words and concepts have been made for WordNet { { cite journal", "entity": [{"entity": "WordNet", "entity_type": "product", "pos": [67, 74]}], "task": "NER"}
{"text": "In red-green anaglyph , the audience was presented three reels of tests , which included rural scenes , test shots of Marie Doro , a segment of John B. Mason playing a number of passages from Jim the Penman ( a film released by Famous Players-Lasky that year , but not in 3D ) , Oriental dancers , and a reel of footage of Niagara Falls .", "entity": [{"entity": "red-green anaglyph", "entity_type": "else", "pos": [3, 21]}, {"entity": "Marie Doro", "entity_type": "person", "pos": [118, 128]}, {"entity": "John B. Mason", "entity_type": "person", "pos": [144, 157]}, {"entity": "Jim the Penman", "entity_type": "person", "pos": [192, 206]}, {"entity": "Famous Players-Lasky", "entity_type": "organization", "pos": [228, 248]}, {"entity": "Niagara Falls", "entity_type": "location", "pos": [323, 336]}], "task": "NER"}
{"text": "This is a particular way of implementing maximum likelihood estimation for this problem .", "entity": [{"entity": "maximum likelihood estimation", "entity_type": "metrics", "pos": [41, 70]}], "task": "NER"}
{"text": "Crawler-friendly Web Servers , and it integrates the features of sitemaps and RSS feeds into a decentralized mechanism for computational biologists and bio-informaticians to openly broadcast and retrieve meta-data about biomedical resources .", "entity": [{"entity": "Crawler-friendly Web Servers", "entity_type": "product", "pos": [0, 28]}, {"entity": "RSS", "entity_type": "else", "pos": [78, 81]}], "task": "NER"}
{"text": "It is covered by American National Standards Institute / NISO standard Z39.50 , and International Organization for Standardization standard 23950 .", "entity": [{"entity": "American National Standards Institute / NISO standard Z39.50", "entity_type": "else", "pos": [17, 77]}, {"entity": "International Organization for Standardization standard 23950", "entity_type": "else", "pos": [84, 145]}], "task": "NER"}
{"text": "The encoder and decoder are trained to take a phrase and reproduce the one-hot distribution of a corresponding paraphrase by minimizing perplexity using simple stochastic gradient descent .", "entity": [{"entity": "one-hot distribution", "entity_type": "else", "pos": [71, 91]}, {"entity": "perplexity", "entity_type": "metrics", "pos": [136, 146]}, {"entity": "stochastic gradient descent", "entity_type": "algorithm", "pos": [160, 187]}], "task": "NER"}
{"text": "Other typical applications of pattern recognition techniques are automatic speech recognition , classification of text into several categories ( e.g. , spam / non-spam email messages ) , the handwriting recognition on postal envelopes , automatic recognition of images of human faces , or handwriting image extraction from medical forms .", "entity": [{"entity": "pattern recognition", "entity_type": "field", "pos": [30, 49]}, {"entity": "automatic speech recognition", "entity_type": "task", "pos": [65, 93]}, {"entity": "classification of text into several categories", "entity_type": "task", "pos": [96, 142]}, {"entity": "handwriting recognition on postal envelopes", "entity_type": "task", "pos": [191, 234]}, {"entity": "automatic recognition of images of human faces", "entity_type": "task", "pos": [237, 283]}, {"entity": "handwriting image extraction from medical forms", "entity_type": "task", "pos": [289, 336]}], "task": "NER"}
{"text": "Artificial neural networks have been used on a variety of tasks , including computer vision , speech recognition , machine translation , social network filtering , playing board and video games and medical diagnosis .", "entity": [{"entity": "Artificial neural networks", "entity_type": "algorithm", "pos": [0, 26]}, {"entity": "computer vision", "entity_type": "field", "pos": [76, 91]}, {"entity": "speech recognition", "entity_type": "task", "pos": [94, 112]}, {"entity": "machine translation", "entity_type": "task", "pos": [115, 134]}, {"entity": "social network filtering", "entity_type": "task", "pos": [137, 161]}, {"entity": "playing board and video games", "entity_type": "task", "pos": [164, 193]}, {"entity": "medical diagnosis", "entity_type": "task", "pos": [198, 215]}], "task": "NER"}
{"text": "Examples include Salford Systems CART ( which licensed the proprietary code of the original CART authors ) , IBM SPSS Modeler , RapidMiner , SAS Enterprise Miner , Matlab , R ( an open-source software environment for statistical computing , which includes several CART implementations such as rpart , party and randomForest packages ) , Weka ( a free and open-source data-mining suite , contains many decision tree algorithms ) , Orange , KNIME , Microsoft SQL Server programming language ) .", "entity": [{"entity": "Salford Systems", "entity_type": "organization", "pos": [17, 32]}, {"entity": "CART", "entity_type": "product", "pos": [33, 37]}, {"entity": "CART", "entity_type": "product", "pos": [92, 96]}, {"entity": "IBM", "entity_type": "organization", "pos": [109, 112]}, {"entity": "SPSS Modeler", "entity_type": "product", "pos": [113, 125]}, {"entity": "RapidMiner", "entity_type": "product", "pos": [128, 138]}, {"entity": "SAS Enterprise Miner", "entity_type": "product", "pos": [141, 161]}, {"entity": "Matlab", "entity_type": "product", "pos": [164, 170]}, {"entity": "R", "entity_type": "program language", "pos": [173, 174]}, {"entity": "statistical computing", "entity_type": "field", "pos": [217, 238]}, {"entity": "CART", "entity_type": "product", "pos": [264, 268]}, {"entity": "rpart", "entity_type": "algorithm", "pos": [293, 298]}, {"entity": "party", "entity_type": "algorithm", "pos": [301, 306]}, {"entity": "randomForest", "entity_type": "algorithm", "pos": [311, 323]}, {"entity": "Weka", "entity_type": "product", "pos": [337, 341]}, {"entity": "data-mining", "entity_type": "task", "pos": [367, 378]}, {"entity": "decision tree", "entity_type": "algorithm", "pos": [401, 414]}, {"entity": "Orange", "entity_type": "product", "pos": [430, 436]}, {"entity": "KNIME", "entity_type": "product", "pos": [439, 444]}, {"entity": "Microsoft SQL Server", "entity_type": "product", "pos": [447, 467]}], "task": "NER"}
{"text": "Linear predictive coding ( LPC ) was first developed by Fumitada Itakura of Nagoya University and Shuzo Saito of Nippon Telegraph and Telephone ( NTT ) in 1966 , and then further developed by Bishnu S. Atal and Manfred R. Schroeder at Bell Labs during the early-to-mid-1970s , becoming a basis for the first speech synthesizer DSP chips in the late 1970s .", "entity": [{"entity": "Linear predictive coding", "entity_type": "algorithm", "pos": [0, 24]}, {"entity": "LPC", "entity_type": "algorithm", "pos": [27, 30]}, {"entity": "Fumitada Itakura", "entity_type": "researcher", "pos": [56, 72]}, {"entity": "Nagoya University", "entity_type": "university", "pos": [76, 93]}, {"entity": "Shuzo Saito", "entity_type": "researcher", "pos": [98, 109]}, {"entity": "Nippon Telegraph and Telephone", "entity_type": "organization", "pos": [113, 143]}, {"entity": "NTT", "entity_type": "organization", "pos": [146, 149]}, {"entity": "Bishnu S. Atal", "entity_type": "researcher", "pos": [192, 206]}, {"entity": "Manfred R. Schroeder", "entity_type": "researcher", "pos": [211, 231]}, {"entity": "Bell Labs", "entity_type": "organization", "pos": [235, 244]}, {"entity": "speech synthesizer DSP chips", "entity_type": "product", "pos": [308, 336]}], "task": "NER"}
{"text": "An F-score is a combination of the precision and the recall , providing a single score .", "entity": [{"entity": "F-score", "entity_type": "metrics", "pos": [3, 10]}, {"entity": "precision", "entity_type": "metrics", "pos": [35, 44]}, {"entity": "recall", "entity_type": "metrics", "pos": [53, 59]}], "task": "NER"}
{"text": "Image analysis tasks can be as simple as reading bar code d tags or as sophisticated as facial recognition system .", "entity": [{"entity": "Image analysis", "entity_type": "field", "pos": [0, 14]}, {"entity": "reading bar code d tags", "entity_type": "task", "pos": [41, 64]}, {"entity": "facial recognition system", "entity_type": "product", "pos": [88, 113]}], "task": "NER"}
{"text": "The special case of linear support-vector machines can be solved more efficiently by the same kind of algorithms to optimize its close cousin , logistic regression ; this class of algorithms includes Stochastic gradient descent ( e.g. , PEGASOS ) .", "entity": [{"entity": "support-vector machines", "entity_type": "algorithm", "pos": [27, 50]}, {"entity": "logistic regression", "entity_type": "algorithm", "pos": [144, 163]}, {"entity": "Stochastic gradient descent", "entity_type": "algorithm", "pos": [200, 227]}, {"entity": "PEGASOS", "entity_type": "algorithm", "pos": [237, 244]}], "task": "NER"}
{"text": "When Siri on an iOS device is asked Do you have a pet ? , one the responses is I used to have an AIBO .", "entity": [{"entity": "Siri", "entity_type": "product", "pos": [5, 9]}, {"entity": "iOS", "entity_type": "product", "pos": [16, 19]}, {"entity": "AIBO", "entity_type": "product", "pos": [97, 101]}], "task": "NER"}
{"text": "In information retrieval , the positive predictive value is called precision , and sensitivity is called recall .", "entity": [{"entity": "information retrieval", "entity_type": "task", "pos": [3, 24]}, {"entity": "positive predictive value", "entity_type": "metrics", "pos": [31, 56]}, {"entity": "precision", "entity_type": "metrics", "pos": [67, 76]}, {"entity": "sensitivity", "entity_type": "metrics", "pos": [83, 94]}, {"entity": "recall", "entity_type": "metrics", "pos": [105, 111]}], "task": "NER"}
{"text": "In particular , his research focused on areas such as text mining ( extraction , categorization , novelty detection ) and in new theoretical frameworks such as a unified utility-based theory bridging information retrieval , Automatic summarization , free-text Question Answering and related tasks .", "entity": [{"entity": "text mining", "entity_type": "field", "pos": [54, 65]}, {"entity": "extraction", "entity_type": "task", "pos": [68, 78]}, {"entity": "categorization", "entity_type": "task", "pos": [81, 95]}, {"entity": "novelty detection", "entity_type": "task", "pos": [98, 115]}, {"entity": "information retrieval", "entity_type": "task", "pos": [200, 221]}, {"entity": "Automatic summarization", "entity_type": "task", "pos": [224, 247]}, {"entity": "free-text Question Answering", "entity_type": "task", "pos": [250, 278]}], "task": "NER"}
{"text": "Delta robot s have base-mounted rotary actuator s that move a light , stiff , parallelogram arm .", "entity": [{"entity": "Delta robot", "entity_type": "product", "pos": [0, 11]}, {"entity": "rotary actuator", "entity_type": "product", "pos": [32, 47]}, {"entity": "parallelogram arm", "entity_type": "else", "pos": [78, 95]}], "task": "NER"}
{"text": "The four outcomes can be formulated in a 2 × 2 contingency table or confusion matrix , as follows :", "entity": [{"entity": "2 × 2 contingency table", "entity_type": "metrics", "pos": [41, 64]}, {"entity": "confusion matrix", "entity_type": "metrics", "pos": [68, 84]}], "task": "NER"}
{"text": "The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract unknown , interesting patterns such as groups of data records ( cluster analysis ) , unusual records ( anomaly detection ) , and dependencies ( association rule mining , sequential pattern mining ) .", "entity": [{"entity": "data mining", "entity_type": "field", "pos": [11, 22]}, {"entity": "cluster analysis", "entity_type": "task", "pos": [175, 191]}, {"entity": "anomaly detection", "entity_type": "task", "pos": [214, 231]}, {"entity": "association rule mining", "entity_type": "task", "pos": [255, 278]}, {"entity": "sequential pattern mining", "entity_type": "task", "pos": [281, 306]}], "task": "NER"}
{"text": "For a recommender system , sentiment analysis has been proven to be a valuable technique .", "entity": [{"entity": "recommender system", "entity_type": "product", "pos": [6, 24]}, {"entity": "sentiment analysis", "entity_type": "task", "pos": [27, 45]}], "task": "NER"}
{"text": "By chance , the Germans had chosen the operating frequency of the Wotan system very badly ; it operated on 45 MHz , which just happened to be the frequency of the powerful-but-dormant BBC television transmitter at Alexandra Palace .", "entity": [{"entity": "Germans", "entity_type": "else", "pos": [16, 23]}, {"entity": "Wotan", "entity_type": "product", "pos": [66, 71]}, {"entity": "BBC", "entity_type": "organization", "pos": [184, 187]}, {"entity": "Alexandra Palace", "entity_type": "location", "pos": [214, 230]}], "task": "NER"}
{"text": "In Semantic Web applications , and in relatively popular applications of RDF like RSS and FOAF ( Friend a Friend ) , resources tend to be represented by URIs that intentionally denote , and can be used to access , actual data on the World Wide Web .", "entity": [{"entity": "Semantic Web applications", "entity_type": "else", "pos": [3, 28]}, {"entity": "RDF", "entity_type": "else", "pos": [73, 76]}, {"entity": "RSS", "entity_type": "product", "pos": [82, 85]}, {"entity": "FOAF", "entity_type": "product", "pos": [90, 94]}, {"entity": "Friend a Friend", "entity_type": "product", "pos": [97, 112]}, {"entity": "URIs", "entity_type": "else", "pos": [153, 157]}, {"entity": "World Wide Web", "entity_type": "product", "pos": [233, 247]}], "task": "NER"}
{"text": "The Association for the Advancement of Artificial Intelligence has studied this topic in depth", "entity": [{"entity": "Association for the Advancement of Artificial Intelligence", "entity_type": "conference", "pos": [4, 62]}], "task": "NER"}
{"text": "Starting as a curiosity , the speech system of Apple Macintosh has evolved into a fully supported program PlainTalk , for people with vision problems .", "entity": [{"entity": "speech system of Apple Macintosh", "entity_type": "product", "pos": [30, 62]}, {"entity": "PlainTalk", "entity_type": "product", "pos": [106, 115]}], "task": "NER"}
{"text": "Other areas of usage for ontologies within NLP include information retrieval , information extraction and automatic summarization .", "entity": [{"entity": "NLP", "entity_type": "field", "pos": [43, 46]}, {"entity": "information retrieval", "entity_type": "task", "pos": [55, 76]}, {"entity": "information extraction", "entity_type": "task", "pos": [79, 101]}, {"entity": "automatic summarization", "entity_type": "task", "pos": [106, 129]}], "task": "NER"}
{"text": "The Institute has collaborated closely with the Janelia Farm Campus of Howard Hughes Medical Institute , the Allen Institute for Brain Science and the National Institutes of Health to develop better methods of reconstructing neuronal architectures .", "entity": [{"entity": "Janelia Farm Campus of Howard Hughes Medical Institute", "entity_type": "organization", "pos": [48, 102]}, {"entity": "Allen Institute for Brain Science", "entity_type": "organization", "pos": [109, 142]}, {"entity": "National Institutes of Health", "entity_type": "organization", "pos": [151, 180]}], "task": "NER"}
{"text": "Recently , Google announced that Google Translate translates roughly enough text to fill 1 million books in one day ( 2012 ) .", "entity": [{"entity": "Google", "entity_type": "organization", "pos": [11, 17]}, {"entity": "Google Translate", "entity_type": "product", "pos": [33, 49]}], "task": "NER"}
{"text": "Events are held worldwide , and are most popular in the United Kingdom , United States , Japan , Singapore , India , South Korea and becoming popular in subcontinent countries such as Sri Lanka .", "entity": [{"entity": "United Kingdom", "entity_type": "country", "pos": [56, 70]}, {"entity": "United States", "entity_type": "country", "pos": [73, 86]}, {"entity": "Japan", "entity_type": "country", "pos": [89, 94]}, {"entity": "Singapore", "entity_type": "country", "pos": [97, 106]}, {"entity": "India", "entity_type": "country", "pos": [109, 114]}, {"entity": "South Korea", "entity_type": "country", "pos": [117, 128]}, {"entity": "Sri Lanka", "entity_type": "country", "pos": [184, 193]}], "task": "NER"}
{"text": "These packages are developed primarily in R , and sometimes in Java , C , C + + , and Fortran .", "entity": [{"entity": "R", "entity_type": "program language", "pos": [42, 43]}, {"entity": "Java", "entity_type": "program language", "pos": [63, 67]}, {"entity": "C", "entity_type": "program language", "pos": [70, 71]}, {"entity": "C + +", "entity_type": "program language", "pos": [74, 79]}, {"entity": "Fortran", "entity_type": "program language", "pos": [86, 93]}], "task": "NER"}
{"text": "As part of the 2006 European Conference on Computer Vision ( ECCV ) , Dalal and Triggs teamed up with Cordelia Schmid to apply HOG detectors to the problem of human detection in films and videos .", "entity": [{"entity": "2006 European Conference on Computer Vision", "entity_type": "conference", "pos": [15, 58]}, {"entity": "ECCV", "entity_type": "conference", "pos": [61, 65]}, {"entity": "Dalal", "entity_type": "researcher", "pos": [70, 75]}, {"entity": "Triggs", "entity_type": "researcher", "pos": [80, 86]}, {"entity": "Cordelia Schmid", "entity_type": "researcher", "pos": [102, 117]}, {"entity": "HOG detectors", "entity_type": "algorithm", "pos": [127, 140]}, {"entity": "human detection in films and videos", "entity_type": "task", "pos": [159, 194]}], "task": "NER"}
{"text": "In addition to sensitivity and specificity , the performance of a binary classification test can be measured with positive predictive value ( PPV ) , also known as precision , and negative predictive value ( NPV ) .", "entity": [{"entity": "sensitivity", "entity_type": "metrics", "pos": [15, 26]}, {"entity": "specificity", "entity_type": "metrics", "pos": [31, 42]}, {"entity": "binary classification", "entity_type": "task", "pos": [66, 87]}, {"entity": "positive predictive value", "entity_type": "metrics", "pos": [114, 139]}, {"entity": "PPV", "entity_type": "metrics", "pos": [142, 145]}, {"entity": "precision", "entity_type": "metrics", "pos": [164, 173]}, {"entity": "negative predictive value", "entity_type": "metrics", "pos": [180, 205]}, {"entity": "NPV", "entity_type": "metrics", "pos": [208, 211]}], "task": "NER"}
{"text": "Such models may given partial credit for overlapping matches ( such as using the Jaccard index criterion .", "entity": [{"entity": "Jaccard index criterion", "entity_type": "metrics", "pos": [81, 104]}], "task": "NER"}
{"text": "Further , in the case of estimation based on a single sample , it demonstrates philosophical issues and possible misunderstandings in the use of maximum likelihood estimators and likelihood functions .", "entity": [{"entity": "maximum likelihood estimators and likelihood functions", "entity_type": "metrics", "pos": [145, 199]}], "task": "NER"}