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You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000). While earlier work in this direction concentrated on manual construction of rules, most of the recent work has been motivated by the observation that shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part-of-speech information. to ] [NP only $ 1.8 billion ] [PP in ] [NP September] . Citation Sentence: Thus , over the past few years , along with advances in the use of learning and statistical methods for acquisition of full parsers ( Collins , 1997 ; Charniak , 1997a ; Charniak , 1997b ; Ratnaparkhi , 1997 ) , significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship ( Church , 1988 ; Ramshaw and Marcus , 1995 ; Argamon et al. , 1998 ; Cardie and Pierce , 1998 ; Munoz et al. , 1999 ; Punyakanok and Roth , 2001 ; Buchholz et al. , 1999 ; Tjong Kim Sang and Buchholz , 2000 ) . Context after the citation: Research on shallow parsing was inspired by psycholinguistics arguments (Gee and Grosjean, 1983) that suggest that in many scenarios (e.g., conversational) full parsing is not a realistic strategy for sentence processing and analysis, and was further motivated by several arguments from a natural language engineering viewpoint. First, it has been noted that in many natural language applications it is sufficient to use shallow parsing information; information such as noun phrases (NPs) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization (Grishman, 1995; Appelt et al., 1993). Second, while training a full parser requires a collection of fully parsed sentences as training corpus, it is possible to train a shallow parser incrementally. If all that is available is a collection of sentences annotated for NPs, it can be used to produce this level of analysis.

Background

acl_arc_intent_classification:train:0

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: This was done by MERT optimization (Och, 2003) towards post-edits under the TER target metric. PE-dev was used to obtain w* to define the utility model. PE-test was held out for testing the algorithms’ progress on unseen data. Citation Sentence: This was done by MERT optimization ( Och , 2003 ) towards post-edits under the TER target metric . Context after the citation: Note that the goal of our experi- ments is not to improve SMT performance over any algorithm that has access to full information to compute w*. Rather, we want to show that learning from weak feedback leads to convergence in regret with respect to the optimal model, albeit at a slower rate than learning from strong feedback. The feedback data in this experiment were generated by searching the n-best list for translations that are α-informative at α E 10.1, 0.5, 1.01 (with possible non-zero slack).

Uses

acl_arc_intent_classification:train:1

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Similar to our previous work (Chan and Ng, 2005b), we used the supervised WSD approach described in (Lee and Ng, 2002) for our experiments, using the naive Bayes algorithm as our classifier. Citation Sentence: Similar to our previous work ( Chan and Ng , 2005b ) , we used the supervised WSD approach described in ( Lee and Ng , 2002 ) for our experiments , using the naive Bayes algorithm as our classifier . Context after the citation: Knowledge sources used include partsof-speech, surrounding words, and local collocations. This approach achieves state-of-the-art accuracy. All accuracies reported in our experiments are micro-averages over all test examples. In (Chan and Ng, 2005b), we used a multiclass naive Bayes classifier (denoted by NB) for each word.

Uses

acl_arc_intent_classification:train:10

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: As (Daelemans et al., 1999) show, lexical information improves on NP and VP chunking as well. It is not aimed at handling dependencies, which require heavy use of lexical information (Hindle and Rooth, 1993, for PP attachment). The presented method concerns primarily with phrases, which can be represented by a tree structure. Citation Sentence: As ( Daelemans et al. , 1999 ) show , lexical information improves on NP and VP chunking as well . Context after the citation: Since our method uses raw data, representing lexical entries will require a lot of memory. In a future work, we plan to use the system for providing instance candidates, and disambiguate them using an algorithm more suitable for handling lexical information. An additional possibility is to use word-types, such as a special tag for be-verbs, or for prepositions like 'of' which attaches mainly to nouns (Sekine and Grishman, 1995). In a similar vain to Skut and Brants (1998) and Buchholz et al. (1999), the method extends an existing flat shallow-parsing method to handle composite structures.

FutureWork

acl_arc_intent_classification:train:100

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Some previous works (Bannard and Callison-Burch, 2005; Zhao et al., 2009; Kouylekov et al., 2009) indicate, as main limitations of the mentioned resources, their limited coverage, their low precision, and the fact that they are mostly suitable to capture relations mainly between single words. As emerges from the ablation tests reported in (Bentivogli et al., 2010), even the most common resources proved to have a positive impact on some systems and a negative impact on others. Despite the consensus on the usefulness of lexical knowledge for textual inference, determining the actual impact of these resources is not straightforward, as they always represent one component in complex architectures that may use them in different ways. Citation Sentence: Some previous works ( Bannard and Callison-Burch , 2005 ; Zhao et al. , 2009 ; Kouylekov et al. , 2009 ) indicate , as main limitations of the mentioned resources , their limited coverage , their low precision , and the fact that they are mostly suitable to capture relations mainly between single words . Context after the citation: Addressing CLTE we have to face additional and more problematic issues related to: i) the stronger need of lexical knowledge, and ii) the limited availability of multilingual lexical resources. As regards the first issue, it’s worth noting that in the monolingual scenario simple “bag of words” (or “bag of ngrams”) approaches are per se sufficient to achieve results above baseline. In contrast, their application in the cross-lingual setting is not a viable solution due to the impossibility to perform direct lexical matches between texts and hypotheses in different languages. This situation makes the availability of multilingual lexical knowledge a necessary condition to bridge the language gap.

Background

acl_arc_intent_classification:train:1000

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Nugget F-score has been employed as a metric in the TREC question-answering track since 2003, to evaluate so-called definition and “other” questions (Voorhees 2003). Recently, there is a growing consensus that an evaluation methodology based on the notion of “information nuggets” may provide an appropriate framework for assessing the quality of answers to complex questions. In Sections 9 and 10, we have discussed many of these issues. Citation Sentence: Nugget F-score has been employed as a metric in the TREC question-answering track since 2003 , to evaluate so-called definition and `` other '' questions ( Voorhees 2003 ) . Context after the citation: A number of studies (e.g., Hildebrandt, Katz, and Lin 2004) have pointed out shortcomings of the original nugget scoring model, although a number of these issues have been recently addressed (Lin and Demner-Fushman 2005a, 2006b). However, adaptation of the nugget evaluation methodology to a domain as specific as clinical medicine is an endeavor that has yet to be undertaken.

Background

acl_arc_intent_classification:train:1001

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Manually defined heuristics are used to automatically annotate each tree in the treebank with partially specified HPSG derivation trees: Head/argument/modifier distinctions are made for each node in the tree based on Magerman (1994) and Collins (1997); Miyao, Ninomiya, and Tsujii (2004) and Nakanishi, Miyao, and Tsujii (2004) describe a methodology for acquiring an English HPSG from the Penn-II Treebank. Unlike our approach, those of Xia (1999) and Hockenmaier, Bierner, and Baldridge (2004) include a substantial initial correction and clean-up of the Penn-II trees. Citation Sentence: Manually defined heuristics are used to automatically annotate each tree in the treebank with partially specified HPSG derivation trees : Head/argument/modifier distinctions are made for each node in the tree based on Magerman ( 1994 ) and Collins ( 1997 ) ; Context after the citation: the whole tree is then converted to a binary tree; heuristics are applied to deal with phenomena such as LDDs and coordination and to correct some errors in the treebank, and finally an HPSG category is assigned to each node in the tree in accordance with its CFG category. In the next phase of the process (externalization), HPSG lexical entries are automatically extracted from the annotated trees through the application of “inverse schemata.”

Uses

acl_arc_intent_classification:train:1002

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Regarding future work, there are many research line that may be followed: i) Capturing more features by employing external knowledge such as ontological, lexical resource or WordNet-based features (Basili et al., 2005a; Basili et al., 2005b; Bloehdorn et al., 2006; Bloehdorn and Moschitti, 2007) or shallow semantic trees, (Giuglea and Moschitti, 2004; Giuglea and Moschitti, 2006; Moschitti and Bejan, 2004; Moschitti et al., 2007; Moschitti, 2008; Moschitti et al., 2008). Our novel composite kernels, which account for the two syntactic structures, are experimented with the appropriate convolution kernels and show significant improvement with respect to the state-ofthe-art in RE. For the design of automatic relation classifiers, we have investigated the impact of dependency structures to the RE task. Citation Sentence: Regarding future work , there are many research line that may be followed : i ) Capturing more features by employing external knowledge such as ontological , lexical resource or WordNet-based features ( Basili et al. , 2005a ; Basili et al. , 2005b ; Bloehdorn et al. , 2006 ; Bloehdorn and Moschitti , 2007 ) or shallow semantic trees , ( Giuglea and Moschitti , 2004 ; Giuglea and Moschitti , 2006 ; Moschitti and Bejan , 2004 ; Moschitti et al. , 2007 ; Moschitti , 2008 ; Moschitti et al. , 2008 ) . Context after the citation: ii) Design a new tree-based structures, which combines the information of both constituent and dependency parses. From dependency trees we can extract more precise but also more sparse relationships (which may cause overfit). From constituent trees, we can extract subtrees constituted by non-terminal symbols (grammar symbols), which provide a better generalization (with a risk of underfitting). iii) Design a new kernel which can integrate the advantages of the constituent and dependency tree.

FutureWork

acl_arc_intent_classification:train:1003

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In addition, there has been much work on the application of linguistic and semantic knowledge to information retrieval; see Lin and Demner-Fushman (2006a) for a brief overview. For an overview of systems designed to answer open-domain factoid questions, the TREC QA track overview papers are a good place to start (Voorhees and Tice 1999). In this section, however, we will attempt to draw connections to other clinical information systems (although not necessarily for question answering) and related domain-specific question-answering systems. Citation Sentence: In addition , there has been much work on the application of linguistic and semantic knowledge to information retrieval ; see Lin and Demner-Fushman ( 2006a ) for a brief overview . Context after the citation: The idea that clinical information systems should be sensitive to the practice of evidence-based medicine is not new. Based on analyses of 4,000 MEDLINE citations, Mendonc¸a and Cimino (2001) have studied MeSH terms associated with the four basic clinical tasks of therapy, diagnosis, prognosis, and etiology. The goal was to automatically classify citations for task-specific retrieval, similar in spirit to the Hedges Project (Haynes et al. 1994; Wilczynski, McKibbon, and Haynes 2001). Cimino and Mendonc¸a reported good performance for etiology, diagnosis, and in particular therapy, but not prognosis.

Background

acl_arc_intent_classification:train:1004

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: However, Dunning (1993) claims that the log-likelihood chisquared statistic (G2) is more appropriate for corpus-based NLP. The usual statistic encountered in text books is the Pearson chisquared statistic, denoted X2. There is a choice of which statistic to use in conjunction with the test. Citation Sentence: However , Dunning ( 1993 ) claims that the log-likelihood chisquared statistic ( G2 ) is more appropriate for corpus-based NLP . Context after the citation: In Section 6, we compare the two statistics in a task-based evaluation. For Table 2, the value of G2 is 3.8 and the value of X2 is 2.5. Assuming a level of significance of a = 0.05, the critical value is 12.6 (for 6 degrees of freedom). Thus, for this a value, the null hypothesis would not be rejected for either statistic, and the conclusion would be that there is no reason to suppose p(runl (canine), subj) is not a reasonable approximation of p(rtml (dog), subj).

Motivation

acl_arc_intent_classification:train:1005

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: In a log-linear parameterization, for example, a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting (Chen and Rosenfeld, 1999). Maximum-posterior estimation tries to maximize P(0)·Hi fθ(xi, yi) where P(0) is a prior probability. Maximum-likelihood estimation guesses 0ˆ to be the 0 maximizing Hi fθ(xi, yi). Citation Sentence: In a log-linear parameterization , for example , a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting ( Chen and Rosenfeld , 1999 ) . Context after the citation: The EM algorithm (Dempster et al., 1977) can maximize these functions. Roughly, the E step guesses hidden information: if (xi, yi) was generated from the current fθ, which FST paths stand a chance of having been the path used? (Guessing the path also guesses the exact input and output.) The M step updates 0 to make those paths more likely.

Uses

acl_arc_intent_classification:train:1006

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This alignment is done on the basis of both length (Gale and Church [7]) and a notion of cognateness (Simard [16]). After identification of word and sentence boundaries the text is processed into a bi-text by an alignment program. An aligner. Citation Sentence: This alignment is done on the basis of both length ( Gale and Church [ 7 ] ) and a notion of cognateness ( Simard [ 16 ] ) . Context after the citation: 2. Transducers. In order to compare numerical expressions, which often diverge in format between given pairs of languages, normalisation toward a common format is required. This is done with transducers (Kaplan and Kay, [10]).

Uses

acl_arc_intent_classification:train:1007

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Since earlier versions of the SNoW based CSCL were used only to identify single phrases (Punyakanok and Roth, 2001; Munoz et al., 1999) and never to identify a collection of several phrases at the same time, as we do here, we also trained and tested it under the exact conditions of CoNLL-2000 (Tjong Kim Sang and Buchholz, 2000) to compare it to other shallow parsers. The outcomes of these classifiers are then combined in a way that satisfies some constraints – non-overlapping constraints in this case – using an efficient constraint satisfaction mechanism that makes use of the confidence in the classifier’s outcomes. Indeed, in CSCL (constraint satisfaction with classifiers), SNoW is used to learn several different classifiers – each detects the beginning or end of a phrase of some type (noun phrase, verb phrase, etc.). Citation Sentence: Since earlier versions of the SNoW based CSCL were used only to identify single phrases ( Punyakanok and Roth , 2001 ; Munoz et al. , 1999 ) and never to identify a collection of several phrases at the same time , as we do here , we also trained and tested it under the exact conditions of CoNLL-2000 ( Tjong Kim Sang and Buchholz , 2000 ) to compare it to other shallow parsers . Context after the citation: Table 1 shows that it ranks among the top shallow parsers evaluated there 1.

Extends

acl_arc_intent_classification:train:1008

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: A more detailed discussion of the various available Arabic tag sets can be found in Habash (2010). These tag sets are hybrids in the sense that they are neither simply the core POS, nor the complete morphologically enriched tag set, but instead they selectively enrich the core POS tag set with only certain morphological features. Therefore, researchers have proposed tag sets for MSA whose size is similar to that of the English PTB tag set, as this has proven to be a useful size computationally. Citation Sentence: A more detailed discussion of the various available Arabic tag sets can be found in Habash ( 2010 ) . Context after the citation: The following are the various tag sets we use in this article: (a) the core POS tag sets CORE44 and the newly introduced CORE12; (b) CATiB Treebank tag set (CATIB6) (Habash and Roth 2009) and its newly introduced extension of CATIBEX created using simple regular expressions on word form, indicating particular morphemes such as the prefix JI Al+ or the suffix v' +wn; this tag set is the best-performing tag set for Arabic on predicted values as reported in Section 4; (c) the PATB full tag set with complete morphological tag (BW) (Buckwalter 2004); and two extensions of the PATB reduced tag set (PENN POS, a.k.a. RTS, size 24 [Diab, Hacioglu, and Jurafsky 2004]), both outperforming it: (d) Kulick, Gabbard, and Marcus (2006)’s tag set (KULICK), size 43, one of whose most important extensions is the marking of the definite article clitic, and (e) Diab and Benajiba’s (in preparation) EXTENDED RTS tag set (ERTS), which marks gender, number, and definiteness, size 134.

Background

acl_arc_intent_classification:train:1009

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: But while Bod's estimator obtains state-of-the-art results on the WSJ, comparable to Charniak (2000) and Collins (2000), Bonnema et al.'s estimator performs worse and is comparable to Collins (1996). We show that these PCFG-reductions result in a 60 times speedup in processing time w.r.t. Bod (2001, 2003). This paper presents the first published results with Goodman's PCFG-reductions of both Bonnema et al.'s (1999) and Bod's (2001) estimators on the WSJ. Citation Sentence: But while Bod 's estimator obtains state-of-the-art results on the WSJ , comparable to Charniak ( 2000 ) and Collins ( 2000 ) , Bonnema et al. 's estimator performs worse and is comparable to Collins ( 1996 ) . Context after the citation: In the second part of this paper, we extend our experiments with a new notion of the best parse tree. Most previous notions of best parse tree in DOP1 were based on a probabilistic metric, with Bod (2000b) as a notable exception, who used a simplicity metric based on the shortest derivation. We show that a combination of a probabilistic and a simplicity metric, which chooses the simplest parse from the n likeliest parses, outperforms the use of these metrics alone.

Background

acl_arc_intent_classification:train:101

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The high Dirichlet priors are chosen to prevent sparsity in topic distributions, while the other parameters are selected as the best from Hoffman et al. (2010). We do not optimize these hyperparameters or vary them over time. In all settings, we fix all Dirichlet priors at 0.1, use a learning rate 0.7, and use minibatch sizes of 1024 documents. Citation Sentence: The high Dirichlet priors are chosen to prevent sparsity in topic distributions , while the other parameters are selected as the best from Hoffman et al. ( 2010 ) . Context after the citation: In order to optimize the number of topics K, we run five trials of each modality for 2000 iterations for K = 150, 100, 150, 200, 2501 (a total of 25 runs per setup). We select the value or K for each model which minimizes the average perplexity estimate over the five trials.

Uses

acl_arc_intent_classification:train:1010

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The resulting speech understanding system is called the Voice Natural Language Computer with Expectation (VNLCE, Fink 1983). The expectation system provided the intermediate processing between the errorful output of the speech recognizer and the deep semantics of NLC. An off-the-shelf speech recognition device, a Nippon Electric Corporation DP-200, was added to an existing natural language processing system, the Natural Language Computer (NLC) (Ballard 1979, Biermann and Ballard 1980). Citation Sentence: The resulting speech understanding system is called the Voice Natural Language Computer with Expectation ( VNLCE , Fink 1983 ) . Context after the citation: [The current system should be distinguished from an earlier voice system (VNLC, Biermann et al. 1985), which had no expectation and which handled discrete speech where a 300 millisecond pause must follow each word.] It should be emphasized, of course, that the central issue here is the study of expectation mechanisms and the details of the design decisions could have been made in rather different ways. Thus one could have implemented expectation error correction with a typed input system or with a speech input system that integrates voice signal processing with higher level functions in a way not possible with a commercial recognizer. This implementation shows only one way in which the functions P, Predicts, Mergeable, and Merge can be constructed to achieve expectation capabilities.

Background

acl_arc_intent_classification:train:1011

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In our previous papers (Zhang and Clark 2011; Zhang, Blackwood, and Clark 2012), we applied a set of beams to this structure, which makes it similar to the data structure used for phrase-based MT decoding (Koehn 2010). Second, although we use the term chart, the structure for accepted hypotheses is not a dynamic programming chart in the same way as for the parsing problem. First, the parsing problem has a fixed word order and is considerably simpler than the word ordering problem we are tackling. Citation Sentence: In our previous papers ( Zhang and Clark 2011 ; Zhang , Blackwood , and Clark 2012 ) , we applied a set of beams to this structure , which makes it similar to the data structure used for phrase-based MT decoding ( Koehn 2010 ) . Context after the citation: However, we will show later that this structure is unnecessary when the model has more discriminative power, and a conceptually simpler single beam can be used. We will also investigate the possibility of applying dynamic-programming-style pruning to the chart. We now give an overview of the training algorithm, which is crucial to both the speed and accuracy of the resulting decoder. CCGBank (Hockenmaier and Steedman 2007) is used to train the model.

CompareOrContrast

acl_arc_intent_classification:train:1012

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A number of speech understanding systems have been developed during the past fifteen years (Barnett et al. 1980, Dixon and Martin 1979, Erman et al. 1980, Haton and Pierrel 1976, Lea 1980, Lowerre and Reddy 1980, Medress 1980, Reddy 1976, Walker 1978, and Wolf and Woods 1980). Citation Sentence: A number of speech understanding systems have been developed during the past fifteen years ( Barnett et al. 1980 , Dixon and Martin 1979 , Erman et al. 1980 , Haton and Pierrel 1976 , Lea 1980 , Lowerre and Reddy 1980 , Medress 1980 , Reddy 1976 , Walker 1978 , and Wolf and Woods 1980 ) . Context after the citation: Most of these efforts concentrated on the interaction between low level information sources from a speech recognizer and a natural language processor to discover the meaning of an input sentence. While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983).

CompareOrContrast

acl_arc_intent_classification:train:1013

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012). Citation Sentence: Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information ( Andrews et al. , 2009 ; Steyvers , 2010 ; Feng and Lapata , 2010b ; Bruni et al. , 2011 ; Silberer and Lapata , 2012 ; Johns and Jones , 2012 ; Bruni et al. , 2012a ; Bruni et al. , 2012b ; Silberer et al. , 2013 ) . Context after the citation: Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the “meaning of words is entirely given by other words” (Bruni et al., 2012b). In this paper, we explore various ways to integrate new perceptual information through novel computational modeling of this grounded knowledge into a multimodal distributional model of word meaning. The model we rely on was originally developed by Andrews et al. (2009) and is based on a generalization of Latent Dirichlet Allocation.

Background

acl_arc_intent_classification:train:1014

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In particular, since we treat each individual speech within a debate as a single “document”, we are considering a version of document-level sentiment-polarity classification, namely, automatically distinguishing between positive and negative documents (Das and Chen, 2001; Pang et al., 2002; Turney, 2002; Dave et al., 2003). Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis, an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language (early work includes Wiebe and Rapaport (1988), Hearst (1992), Sack (1994), and Wiebe (1994); see Esuli (2006) for an active bibliography). Note that from an experimental point of view, this is a very convenient problem to work with because we can automatically determine ground truth (and thus avoid the need for manual annotation) simply by consulting publicly available voting records. Citation Sentence: In particular , since we treat each individual speech within a debate as a single `` document '' , we are considering a version of document-level sentiment-polarity classification , namely , automatically distinguishing between positive and negative documents ( Das and Chen , 2001 ; Pang et al. , 2002 ; Turney , 2002 ; Dave et al. , 2003 ) . Context after the citation: Most sentiment-polarity classifiers proposed in the recent literature categorize each document independently. A few others incorporate various measures of inter-document similarity between the texts to be labeled (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006). Many interesting opinion-oriented documents, however, can be linked through certain relationships that occur in the context of evaluative discussions. For example, we may find textual4 evidence of a high likelihood of agreement be4Because we are most interested in techniques applicable across domains, we restrict consideration to NLP aspects of the problem, ignoring external problem-specific information.

Background

acl_arc_intent_classification:train:1015

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: A number of alignment techniques have been proposed, varying from statistical methods (Brown et al., 1991; Gale and Church, 1991) to lexical methods (Kay and Roscheisen, 1993; Chen, 1993). Aligning English-Chinese parallel texts is already very difficult because of the great differences in the syntactic structures and writing systems of the two languages. Some are highly parallel and easy to align while others can be very noisy. Citation Sentence: A number of alignment techniques have been proposed , varying from statistical methods ( Brown et al. , 1991 ; Gale and Church , 1991 ) to lexical methods ( Kay and Roscheisen , 1993 ; Chen , 1993 ) . Context after the citation: The method we adopted is that of Simard et al. (1992). Because it considers both length similarity and cognateness as alignment criteria, the method is more robust and better able to deal with noise than pure length-based methods. Cognates are identical sequences of characters in corresponding words in two languages. They are commonly found in English and French.

Background

acl_arc_intent_classification:train:1016

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: (Michiels (1982) contains further description and discussion of LDOCE.) Most prominent among these are the rich grammatical subcategorisations of the 60,000 entries, the large amount of information concerning phrasal verbs, noun compounds and idioms, the individual subject, collocational and semantic codes for the entries and the consistent use of a controlled 'core' vocabulary in defining the words throughout the dictionary. We chose to employ LDOCE as the machine readable source to aid the development of a substantial lexicon because this dictionary has several properties which make it uniquely appropriate for use as the core knowledge base of a natural language processing system. Citation Sentence: ( Michiels ( 1982 ) contains further description and discussion of LDOCE . ) Context after the citation: In this paper we focus on the exploitation of the LDOCE grammar coding system; Alshawi et al. (1985) and Alshawi (1987) describe further research in Cambridge utilising different types of information available in LDOCE. The information available in the dictionary is both very rich and diverse, but also typically only semiformalised, as it is intended for human, rather than machine, interpetation. As a consequence the programs we are developing, both to restructure and to exploit this information, need to undergo constant revision as they are being used. The system we describe is not intended for off-line use, where one might attempt to derive, completely automatically, a lexicon for natural language analysis.

Background

acl_arc_intent_classification:train:1017

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Juola (1994, 1997) assumes that words ending in -ed are verbs. That is, using the marker hypothesis method of segmentation, smaller aligned segments can be extracted from the phrasal lexicon without recourse to any detailed parsing techniques or complex co-ocurrence measures. <QUANT> all : tous <PREP> of : d’ <LEX> uses : usages <LEX> asbestos : asbeste Citation Sentence: Juola ( 1994 , 1997 ) assumes that words ending in - ed are verbs . Context after the citation: However, given that verbs are not a closed class, in our approach we do not mark chunks beginning with a verb with any marker category. Instead, we take advantage of the fact that the initial phrasal chunks correspond to rule right-hand sides. That is, for a rule in the Penn Treebank VP −> VBG, NP, PP, we are certain (if the annotators have done their job correctly) that the first word in each of the strings corresponding to this right-hand side is a VBG, that is, a present participle. Given this information, in such cases we tag such words with the <LEX> tag.

CompareOrContrast

acl_arc_intent_classification:train:1018

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For the development of these lists we used a collection of texts of about 300,000 words derived from the New York Times (NYT) corpus that was supplied as training data for the 7th Message Understanding Conference (MUC-7) (Chinchor 1998). These four lists can be acquired completely automatically from raw (unlabeled) texts. • frequent proper name • abbreviation (as opposed to regular word) Citation Sentence: For the development of these lists we used a collection of texts of about 300,000 words derived from the New York Times ( NYT ) corpus that was supplied as training data for the 7th Message Understanding Conference ( MUC-7 ) ( Chinchor 1998 ) . Context after the citation: We used these texts because the approach described in this article was initially designed to be part of a named-entity recognition system (Mikheev, Grover, and Moens 1998) developed for MUC-7. Although the corpus size of 300,000 words can be seen as large, the fact that this corpus does not have to be annotated in any way and that a corpus of similar size can be easily collected from on-line sources (including the Internet) makes this resource cheap to obtain. The first list on which our method relies is a list of common words. This list includes common words for a given language, but no supplementary information such as POS or morphological information is required to be present in this list.

Uses

acl_arc_intent_classification:train:1019

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: In multi-party discussion people usually mention each other’s name for the purpose of disentanglement (Elsner and Charniak, 2008). Another possibly critical feature is the ‘mention of names’. The best baseline ‘Speaker’ in Table 4 also favours this claim. Citation Sentence: In multi-party discussion people usually mention each other 's name for the purpose of disentanglement ( Elsner and Charniak , 2008 ) . Context after the citation: In our corpus we found 175 instances where a participant mentions other participant’s name. In addition to these, ‘Subject of the email’, ‘topic-shift cue words’ can also be beneficial for a model. As a next step for this research, we will investigate how to exploit these features in our methods. We are also interested in the near future to transfer our approach to other similar domains by hierarchical Bayesian multi-task learning and other domain adaptation methods.

Background

acl_arc_intent_classification:train:102

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: We would also provide a user facility for choosing the right licence for every lexia, following the model of Creative Commons licences (Lessig, 2004). We want to model views as dynamic objects the creation of context will be still arbitrary, but changes are very easily. With our typology of links, we aim to solve the framing problem as defined in Section 1.2. Citation Sentence: We would also provide a user facility for choosing the right licence for every lexia , following the model of Creative Commons licences ( Lessig , 2004 ) . Context after the citation:

Uses

acl_arc_intent_classification:train:1020

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The language grounding problem has received significant attention in recent years, owed in part to the wide availability of data sets (e.g. Flickr, Von Ahn (2006)), computing power, improved computer vision models (Oliva and Torralba, 2001; Lowe, 2004; Farhadi et al., 2009; Parikh and Grauman, 2011) and neurological evidence of ties between the language, perceptual and motor systems in the brain (Pulverm¨uller et al., 2005; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006). Citation Sentence: The language grounding problem has received significant attention in recent years , owed in part to the wide availability of data sets ( e.g. Flickr , Von Ahn ( 2006 ) ) , computing power , improved computer vision models ( Oliva and Torralba , 2001 ; Lowe , 2004 ; Farhadi et al. , 2009 ; Parikh and Grauman , 2011 ) and neurological evidence of ties between the language , perceptual and motor systems in the brain ( Pulverm ¨ uller et al. , 2005 ; Tettamanti et al. , 2005 ; Aziz-Zadeh et al. , 2006 ) . Context after the citation: Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead. Some works abstract perception via the usage of symbolic logic representations (Chen et al., 2010; Chen and Mooney, 2011; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), while others choose to employ concepts elicited from psycholinguistic and cognition studies. Within the latter category, the two most common representations have been association norms, where subjects are given a 1http://stephenroller.com/research/ emnlp13

Background

acl_arc_intent_classification:train:1021

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: There has been some controversy, at least for simple stemmers (Lovins, 1968; Porter, 1980), about the effectiveness of morphological analysis for document retrieval (Harman, 1991; Krovetz, 1993; Hull, 1996). Citation Sentence: There has been some controversy , at least for simple stemmers ( Lovins , 1968 ; Porter , 1980 ) , about the effectiveness of morphological analysis for document retrieval ( Harman , 1991 ; Krovetz , 1993 ; Hull , 1996 ) . Context after the citation: The key for quality improvement seems to be rooted mainly in the presence or absence of some form of dictionary. Empirical evidence has been brought forward that inflectional and/or derivational stemmers augmented by dictionaries indeed perform substantially better than those without access to such lexical repositories (Krovetz, 1993; Kraaij and Pohlmann, 1996; Tzoukermann et al., 1997). This result is particularly valid for natural languages with a rich morphology — both in terms of derivation and (single-word) composition. Document retrieval in these languages suffers from serious performance degradation with the stemmingonly query-term-to-text-word matching paradigm.

Background

acl_arc_intent_classification:train:1022

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: We have yet to import such a constraint into our model, but we plan to do so in the near future using the weighted majority algorithm (Littlestone and Warmuth 1992). That is, we want to allocate a larger portion of the probability space to the phrasal and marker lexicons than to the generalized or wordlevel lexicons. We would like to incorporate into our model a procedure whereby translation chunks extracted from the phrasal and marker lexicons are more highly regarded than those constructed by inserting words from the word-level lexicon into generalized marker chunks. Citation Sentence: We have yet to import such a constraint into our model , but we plan to do so in the near future using the weighted majority algorithm ( Littlestone and Warmuth 1992 ) . Context after the citation:

FutureWork

acl_arc_intent_classification:train:1023

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Generally speaking, we find that the personal public diary metaphor behind blogs (McNeill, 2005) may bring to an unsatisfactory representation of the context. So we have tested and compared the most used tools available for blogging: Bloggers, WordPress, MovableType and LiveJournal. While wikis have spread from a detailed design (Cunningham and Leuf, 2001), unfortunately blogs have not been designed under a model. Citation Sentence: Generally speaking , we find that the personal public diary metaphor behind blogs ( McNeill , 2005 ) may bring to an unsatisfactory representation of the context . Context after the citation: The only way to retrieve information is through a search engine or a calendar, i.e. the date of the ‘post’ – a lexia in the jargon of bloggers. Moreover, we use some new web applications to take and share notes or to browser everyone’s bookmarks, e.g. del.icio.us. Mostly, these web applications oriented to writing give a strong emphasis on collaboration and sharing.

Background

acl_arc_intent_classification:train:1024

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Most probabilistic translation model reestimation algorithms published to date are variations on the theme proposed by Brown et al. (1993b). Citation Sentence: Most probabilistic translation model reestimation algorithms published to date are variations on the theme proposed by Brown et al. ( 1993b ) . Context after the citation: These models involve conditional probabilities, but they can be compared to symmetric models if the latter are normalized by the appropriate marginal distribution. I shall review these models using the notation in Table 1. 4.3.1 Models Using Only Co-occurrence Information. Brown and his colleagues em-

Background

acl_arc_intent_classification:train:1025

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments (Dagan and Itai 1990, 1991; Lappin and Leass 1994; Nasukawa 1994; Kennedy and Boguraev 1996; Williams, Harvey, and Preston 1996; Baldwin 1997; Mitkov 1996, 1998b). However, the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies. Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge (Sidner 1979; Carter 1987; Rich and LuperFoy 1988; Carbonell and Brown 1988), which was difficult both to represent and to process, and which required considerable human input. Citation Sentence: A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments ( Dagan and Itai 1990 , 1991 ; Lappin and Leass 1994 ; Nasukawa 1994 ; Kennedy and Boguraev 1996 ; Williams , Harvey , and Preston 1996 ; Baldwin 1997 ; Mitkov 1996 , 1998b ) . Context after the citation: The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers, alongside the increasing availability of corpora and other NLP resources (e.g., ontologies). In fact, the availability of corpora, both raw and annotated with coreferential links, provided a strong impetus to anaphora resolu- tion with regard to both training and evaluation. Corpora (especially when annotated) are an invaluable source not only for empirical research but also for automated learning (e.g., machine learning) methods aiming to develop new rules and approaches; they also provide an important resource for evaluation of the implemented approaches.

Background

acl_arc_intent_classification:train:1026

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The significance testing is performed by paired bootstrap re-sampling (Koehn, 2004). In our experiments the translation performances are measured by case-insensitive BLEU4 metric (Papineni et al., 2002) and we use mtevalv13a.pl as the evaluation tool. We train a 4-gram language model on the Xinhua portion of the English Gigaword corpus using the SRILM Toolkits (Stolcke, 2002) with modified Kneser-Ney smoothing (Chen and Goodman, 1998). Citation Sentence: The significance testing is performed by paired bootstrap re-sampling ( Koehn , 2004 ) . Context after the citation: We use an in-house developed hierarchical phrase-based translation (Chiang, 2005) as our baseline system, and we denote it as In-Hiero. To obtain satisfactory baseline performance, we tune InHiero system for 5 times using MERT, and then se- (MERT). NIST05 is the set used to tune A for MBUU and EBUU, and NIST06 and NIST08 are test sets.

Uses

acl_arc_intent_classification:train:1027

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The system is implemented based on (Galley et al., 2006) and (Marcu et al. 2006). The translation system used for testing the effectiveness of our U-trees is our in-house stringto-tree system (abbreviated as s2t). To create the baseline system, we use the opensource Joshua 4.0 system (Ganitkevitch et al., 2012) to build a hierarchical phrase-based (HPB) system, and a syntax-augmented MT (SAMT) 11 system (Zollmann and Venugopal, 2006) respectively. Citation Sentence: The system is implemented based on ( Galley et al. , 2006 ) and ( Marcu et al. 2006 ) . Context after the citation: In the system, we extract both the minimal GHKM rules (Galley et al., 2004), and the rules of SPMT Model 1 (Galley et al., 2006) with phrases up to length L=5 on the source side. We then obtain the composed rules by composing two or three adjacent minimal rules. To build the above s2t system, we first use the parse tree, which is generated by parsing the English side of the bilingual data with the Berkeley parser (Petrov et al., 2006). Then, we binarize the English parse trees using the head binarization approach (Wang et al., 2007) and use the resulting binary parse trees to build another s2t system.

Uses

acl_arc_intent_classification:train:1028

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: IGEN uses standard chart generation techniques (Kay, 1996) in its base generator to efficiently produce generation candidates. The ranks are determined by the similarity to the closest instances and the highest ranked sentence is chosen as the final generation output. The ranker scores these candidates according to a similarity metric which measures their distance to the elements in the instance base. Citation Sentence: IGEN uses standard chart generation techniques ( Kay , 1996 ) in its base generator to efficiently produce generation candidates . Context after the citation: The set of candidates forms a subset of the chart edges in that they comprise all edges of syntactic category S. The particular advantage of a bottom-up generation algorithm is that it allows our surface-based ranker to score edges as soon as they are built.

Background

acl_arc_intent_classification:train:1029

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Fortunately, indirect associations are usually not difficult to identify, because they tend to be weaker than the direct associations on which they are based (Melamed, 1996c). Models of translational equivalence that are ignorant of indirect associations have &quot;a tendency ... to be confused by collocates&quot; (Dagan et al., 1993). The arrow connecting vk and uk±i in Figure 1 represents an indirect association, since the association between vk and uk±i arises only by virtue of the association between each of them and uk . Citation Sentence: Fortunately , indirect associations are usually not difficult to identify , because they tend to be weaker than the direct associations on which they are based ( Melamed , 1996c ) . Context after the citation: The majority of indirect associations can be filtered out by a simple competition heuristic: Whenever several word tokens ui in one half of the bitext co-occur with a particular word token v in the other half of the bitext, the word that is most likely to be v's translation is the one for which the likelihood L(u, v) of translational equivalence is highest. The competitive linking algorithm implements this heuristic: 1. Discard all likelihood scores for word types deemed unlikely to be mutual translations, i.e. all L(u, v) < 1.

Background

acl_arc_intent_classification:train:103

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Position, subcat frame, phrase type, first word, last word, subcat frame+, predicate, path, head word and its POS, predicate + head word, predicate + phrase type, path to BA and BEI, verb class 3 , verb class + head word, verb class + phrase type, from Xue (2008). Head word POS, Head Word of Prepositional Phrases, Constituent tree distance, from Pradhan etc. (2004). The candidate feature templates include: Voice from Sun and Jurafsky (2004). Citation Sentence: Position , subcat frame , phrase type , first word , last word , subcat frame + , predicate , path , head word and its POS , predicate + head word , predicate + phrase type , path to BA and BEI , verb class 3 , verb class + head word , verb class + phrase type , from Xue ( 2008 ) . Context after the citation: 3 It is a bit different from Xue (2008), since we didn’t use the syntactic alternation information. predicate POS, first word + last word, phrase type of the sibling to the left, phrase type of the sibling to the right, verb + subcate frame+, verb POS + subcat frame+, the amount of VPs in path, phrase type + phrase type of parent node, which can be easily understood by name. voice position, indicates whether the voice marker (BA, BEI) is before or after the constituent in focus. subcat frame*, the rule that expands the parent node of the constituent in focus.

Uses

acl_arc_intent_classification:train:1030

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The psycholinguistic studies of Martin (1970), Allen (1975), Hillinger et al. (1976), Grosjean et al. (1979), Dommergues and Grosjean (1983), and Gee and Grosjean (1983), responding to the idea of readjusted syntax as the source of prosodic phrasing, show that grammatical structure, even if readjusted, is not in itself a reliable predictor of prosodic phrasing: mismatches between syntax and prosody occur often and systematically, and can be related to specific nonsyntactic factors such as length and word frequency. Citation Sentence: The psycholinguistic studies of Martin ( 1970 ) , Allen ( 1975 ) , Hillinger et al. ( 1976 ) , Grosjean et al. ( 1979 ) , Dommergues and Grosjean ( 1983 ) , and Gee and Grosjean ( 1983 ) , responding to the idea of readjusted syntax as the source of prosodic phrasing , show that grammatical structure , even if readjusted , is not in itself a reliable predictor of prosodic phrasing : mismatches between syntax and prosody occur often and systematically , and can be related to specific nonsyntactic factors such as length and word frequency . Context after the citation: For example, although prosodic boundaries between subject and verb do occur, there also exist prosodic patterns in which the boundary comes between the verb and object, i.e., the data reveal both X(VY) and (XV)Y groupings. Grosjean et al. (1979) claims that such mismatches are due for the most part to constituent length, which interacts with grammatical structure and, in some cases, overrides it. Thus syntactic and prosodic structure match when the major constituents of a sentence are roughly equal in length; for example, the main prosodic phrase break corresponds to the subject-predicate boundary in Waiters who remember well II serve orders correctly. Discrepancies in length throw constituents off balance, and so prosodic phrasing will cross constituent boundaries in order to give the phrases similar lengths; this is the case in Chickens were eating II the remaining green vegetables, where the subject-predicate boundary finds no prosodic correspondent.4 The most explicit version of this approach is the analysis presented in Gee and Grosjean (1983) (henceforth G&G).

Background

acl_arc_intent_classification:train:1031

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: A number of studies (e.g., Hildebrandt, Katz, and Lin 2004) have pointed out shortcomings of the original nugget scoring model, although a number of these issues have been recently addressed (Lin and Demner-Fushman 2005a, 2006b). Nugget F-score has been employed as a metric in the TREC question-answering track since 2003, to evaluate so-called definition and “other” questions (Voorhees 2003). Recently, there is a growing consensus that an evaluation methodology based on the notion of “information nuggets” may provide an appropriate framework for assessing the quality of answers to complex questions. Citation Sentence: A number of studies ( e.g. , Hildebrandt , Katz , and Lin 2004 ) have pointed out shortcomings of the original nugget scoring model , although a number of these issues have been recently addressed ( Lin and Demner-Fushman 2005a , 2006b ) . Context after the citation: However, adaptation of the nugget evaluation methodology to a domain as specific as clinical medicine is an endeavor that has yet to be undertaken.

Background

acl_arc_intent_classification:train:1032

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Nasr (1997) reported that the translation lexicon that our model induced from this tiny bitext accounted for 30% of the word types with precision between 84% and 90%. The model was also used to induce a translation lexicon from a 6200-word corpus of French/English weather reports. translation lexicons with 94% precision and 30% recall, when trained on French/English software manuals totaling about 400,000 words. Citation Sentence: Nasr ( 1997 ) reported that the translation lexicon that our model induced from this tiny bitext accounted for 30 % of the word types with precision between 84 % and 90 % . Context after the citation: Recall drops when there is less training data, because the model refuses to make predictions that it cannot make with confidence. For many applications, this is the desired behavior.

Background

acl_arc_intent_classification:train:1033

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Kinyon and Prolo (2002) describe a simple tool which uses fine-grained rules to identify the arguments of verb occurrences in the Penn-II Treebank. Approaches using treebank-based data as a source for subcategorization information, such as ours, do not predefine the frames to be extracted but rather learn them from the data. The extensive evaluation carried out by Schulte im Walde will be discussed in greater detail in Section 6. Citation Sentence: Kinyon and Prolo ( 2002 ) describe a simple tool which uses fine-grained rules to identify the arguments of verb occurrences in the Penn-II Treebank . Context after the citation: This is made possible by manual examination of more than 150 different sequences of syntactic and functional tags in the treebank. Each of these sequences was categorized as a modifier or argument. Arguments were then mapped to traditional syntactic functions. For example, the tag sequence NP-SBJ denotes a mandatory argument, and its syntactic function is subject.

Background

acl_arc_intent_classification:train:1034

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The contextual interpreter then uses a reference resolution approach similar to Byron (2002), and an ontology mapping mechanism (Dzikovska et al., 2008a) to produce a domain-specific semantic representation of the student’s output. The parser provides a domain-independent semantic representation including high-level word senses and semantic role labels. We use the TRIPS dialogue parser (Allen et al., 2007) to parse the utterances. Citation Sentence: The contextual interpreter then uses a reference resolution approach similar to Byron ( 2002 ) , and an ontology mapping mechanism ( Dzikovska et al. , 2008a ) to produce a domain-specific semantic representation of the student 's output . Context after the citation: Utterance content is represented as a set of extracted objects and relations between them. Negation is supported, together with a heuristic scoping algorithm. The interpreter also performs basic ellipsis resolution. For example, it can determine that in the answer to the question “Which bulbs will be on and which bulbs will be off in this diagram?”

CompareOrContrast

acl_arc_intent_classification:train:1035

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Zhu (2005) maintains a survey of this area. Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). inter-document references in the form of hyperlinks (Agrawal et al., 2003). Citation Sentence: Zhu ( 2005 ) maintains a survey of this area . Context after the citation: Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:1036

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The principle of maximum entropy states that when one searches among probability distributions that model the observed data (evidence), the preferred one is the one that maximizes the entropy (a measure of the uncertainty of the model) (Berger et al., 1996). We use a maximum entropy Markov model (MEMM) classifier. We assign to each token in the text a label indicating whether it starts a specific mention, is inside a specific mention, or is outside any mentions. Citation Sentence: The principle of maximum entropy states that when one searches among probability distributions that model the observed data ( evidence ) , the preferred one is the one that maximizes the entropy ( a measure of the uncertainty of the model ) ( Berger et al. , 1996 ) . Context after the citation: One big advantage of this approach is that it can combine arbitrary and diverse types of information in making a classification decision. Our mention detection system predicts the four labels types associated with a mention through a cascade approach. It first predicts the boundary and the main entity type for each mention. Then, it uses the information regarding the type and boundary in different second-stage classifiers to predict the subtype, the mention level, and the mention class.

Background

acl_arc_intent_classification:train:1037

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: In the context of word alignment, Deng and Byrne (2005) use a state-duration HMM in order to model word-to-phrase translations. The idea of introducing constraints over a model to better guide the learning process has appeared before. Citation Sentence: In the context of word alignment , Deng and Byrne ( 2005 ) use a state-duration HMM in order to model word-to-phrase translations . Context after the citation: The fertility of each source word is implicitly encoded in the durations of the HMM states. Without any restrictions, likelihood prefers to always use longer phrases and the authors try to control this behavior by multiplying every transition probability by a constant ri > 1. This encourages more transitions and hence shorter phrases. For the task of unsupervised dependency parsing, Smith and Eisner (2006) add a constraint of the form “the average length of dependencies should be X” to capture the locality of syntax (at least half of the dependencies are between adjacent words), using a scheme they call structural annealing.

Background

acl_arc_intent_classification:train:1038

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: With respect to the focus on function words, our reordering model is closely related to the UALIGN system (Hermjakob, 2009). These related models treat their lexical items as latent variables to be estimated from training data, while our model uses a fixed set of lexical items that correspond to the class of function words. Our reordering model is closely related to the model proposed by Zhang and Gildea (2005; 2006; 2007a), with respect to conditioning the reordering predictions on lexical items. Citation Sentence: With respect to the focus on function words , our reordering model is closely related to the UALIGN system ( Hermjakob , 2009 ) . Context after the citation: However, UALIGN uses deep syntactic analysis and hand-crafted heuristics in its model.

CompareOrContrast

acl_arc_intent_classification:train:1039

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The work of Sarkar (2001) and Steedman, Osborne, et al. (2003) suggests that co-training can be helpful for statistical parsing. Another technique for making better use of unlabeled data is cotraining (Blum and Mitchell 1998), in which two sufficiently different learners help each other learn by labeling training data for one another. This approach assumes that there are enough existing labeled data to train the individual parsers. Citation Sentence: The work of Sarkar ( 2001 ) and Steedman , Osborne , et al. ( 2003 ) suggests that co-training can be helpful for statistical parsing . Context after the citation: Pierce and Cardie (2001) have shown, in the context of base noun identification, that combining sample selection and cotraining can be an effective learning framework for large-scale training. Similar approaches are being explored for parsing (Steedman, Hwa, et al. 2003; Hwa et al. 2003).

Background

acl_arc_intent_classification:train:104

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: We could also introduce new variables, e.g., nonterminal refinements (Matsuzaki et al., 2005), or secondary links Mid (not constrained by TREE/PTREE) that augment the parse with representations of control, binding, etc. (Sleator and Temperley, 1993; Buch-Kromann, 2006). For projective parsing, it is significantly faster than exact dynamic programming, at the cost of small amounts of search error, We are interested in extending these ideas to phrase-structure and lattice parsing, and in trying other higher-order features, such as those used in parse reranking (Charniak and Johnson, 2005; Huang, 2008) and history-based parsing (Nivre and McDonald, 2008). Belief propagation improves non-projective dependency parsing with features that would make exact inference intractable. Citation Sentence: We could also introduce new variables , e.g. , nonterminal refinements ( Matsuzaki et al. , 2005 ) , or secondary links Mid ( not constrained by TREE/PTREE ) that augment the parse with representations of control , binding , etc. ( Sleator and Temperley , 1993 ; Buch-Kromann , 2006 ) . Context after the citation: Other parsing-like problems that could be attacked with BP appear in syntax-based machine translation. Decoding is very expensive with a synchronous grammar composed with an n-gram language model (Chiang, 2007)—but our footnote 10 suggests that BP might incorporate a language model rapidly. String alignment with synchronous grammars is quite expensive even for simple synchronous formalisms like ITG (Wu, 1997)—but Duchi et al. (2007) show how to incorporate bipartite matching into max-product BP. Finally, we can take advantage of improvements to BP proposed in the context of other applications.

FutureWork

acl_arc_intent_classification:train:1040

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Selectional Preferences have also been a recent focus of researchers investigating the learning of paraphrases and inference rules (Pantel et al., 2007; Roberto et al., 2007). Similarity-smoothed models are simple to compute, potentially adaptable to new domains, and require no manually-compiled resources such as WordNet. Erk (2007) compared a number of techniques for creating similar-word sets and found that both the Jaccard coefficient and Lin (1998a)’s information-theoretic metric work best. Citation Sentence: Selectional Preferences have also been a recent focus of researchers investigating the learning of paraphrases and inference rules ( Pantel et al. , 2007 ; Roberto et al. , 2007 ) . Context after the citation: Inferences such as “[X wins Y] ⇒ [X plays Y]” are only valid for certain arguments X and Y. We follow Pantel et al. (2007) in using automatically-extracted semantic classes to help characterize plausible arguments. Discriminative techniques are widely used in NLP and have been applied to the related tasks of word prediction and language modeling. Even-Zohar and Roth (2000) use a classifier to predict the most likely word to fill a position in a sentence (in their experiments: a verb) from a set of candidates (sets of verbs), by inspecting the context of the target token (e.g., the presence or absence of a particular nearby word in the sentence).

Background

acl_arc_intent_classification:train:1041

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together (Andrews et al., 2009; Silberer and Lapata, 2012). Our experiments are based on the multimodal extension of Latent Dirichlet Allocation developed by Andrews et al. (2009). Citation Sentence: Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) . Context after the citation: It has also been shown to be useful in joint inference of text with visual attributes obtained using visual classifiers (Silberer et al., 2013). These multimodal LDA models (hereafter, mLDA) have been shown to be qualitatively sensible and highly predictive of several psycholinguistic tasks (Andrews et al., 2009). However, prior work using mLDA is limited to two modalities at a time. In this section, we describe bimodal mLDA and define two methods for extending it to three or more modalities.

Background

acl_arc_intent_classification:train:1042

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Something like this approach is in fact used in some systems (e.g., Elhadad and Robin 1992; PenMan 1989; Hovy 1988a). The planner could supply whatever information is needed to drive the network.' If that were the only goal, we could dispense with the feedback mechanism and simply design some sort of discrimination network (or similar device) to test various features of the information being expressed. Citation Sentence: Something like this approach is in fact used in some systems ( e.g. , Elhadad and Robin 1992 ; PenMan 1989 ; Hovy 1988a ) . Context after the citation: The problem with the discrimination network approach is that it can't handle the range of examples shown here without violating the modularity of the generator. The examples shown here have involved linguistic choices that depend on the plan structure, on other actions in the plan, and on preconditions of the plan. Furthermore, the plan dependencies can involve a nontrivial amount of reasoning (e.g., the example immediately above, which involves the connection between sunshine and enjoying 33 Of course, that still wouldn't allow for examples like the one in Section 4.2, where the planner modifies the plan in response to the work of the linguistic component. being outdoors).

Background

acl_arc_intent_classification:train:1043

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The emphasis on narrativity takes into account the use of blogs as public diaries on the web, that is still the main current interpretation of this literary genre, or metagenre (McNeill, 2005). We believe that this name is clearly understable to every people educated in a European-based culture, and this is why we have chosen it. It resembles the English word ‘novel’ and the French word ‘nuovelle’. Citation Sentence: The emphasis on narrativity takes into account the use of blogs as public diaries on the web , that is still the main current interpretation of this literary genre , or metagenre ( McNeill , 2005 ) . Context after the citation: Furthermore we noticed that blogs and wikis are currently subjected to osmosis, because they have in common the underlying core technology. So blogs are a literary metagenre which started as authored personal diaries or journals. Now they try to collect themselves in so-called ‘blogspheres’. On the other side, wikis started as collective works where each entry is not owned by a single author e.g. Wikipedia (2005).

Background

acl_arc_intent_classification:train:1044

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: For example, it would be helpful to consider strong correspondence between certain English and Chinese words, as in (Wu, 1994). Beside HTML markups, other criteria may also be incorporated. Despite their large length difference, the two 0002 sentences are still aligned as a 1-1 pair, because the sentences in the following 4 alignments (0003 0003; 0004 0004, 0005; 0005 0006; 0006 0007) have rather similar HTML markups and are taken by the program to be the most likely alignments. Citation Sentence: For example , it would be helpful to consider strong correspondence between certain English and Chinese words , as in ( Wu , 1994 ) . Context after the citation: We hope to implement such correspondences in our future research.

FutureWork

acl_arc_intent_classification:train:1045

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The system utilizes several large size biological databases including three NCBI databases (GenPept [11], RefSeq [12], and Entrez GENE [13]), PSD database from Protein Information Resources (PIR) [14], and Citation Sentence: The system utilizes several large size biological databases including three NCBI databases ( GenPept [ 11 ] , RefSeq [ 12 ] , and Entrez GENE [ 13 ] ) , PSD database from Protein Information Resources ( PIR ) [ 14 ] , and Context after the citation: Proceedings of the ACL Interactive Poster and Demonstration Sessions, pages 17–20, Ann Arbor, June 2005. c �2005 Association for Computational Linguistics UniProt [15]. Additionally, several model organism databases or nomenclature databases were used.

Uses

acl_arc_intent_classification:train:1046

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: We take some core ideas from our previous work on mining script information (Regneri et al., 2010). However, they use MSA at the sentence level rather than at the discourse level. Our approach for sentential paraphrase extraction is related to the one introduced by Barzilay and Lee (2003), who also employ multiple sequence alignment (MSA). Citation Sentence: We take some core ideas from our previous work on mining script information ( Regneri et al. , 2010 ) . Context after the citation: In this earlier work, we focused on event structures and their possible realizations in natural language. The corpus used in those experiments were short crowd-sourced descriptions of everyday tasks written in bullet point style. We aligned them with a hand-crafted similarity measure that was specifically designed for this text type. In this current work, we target the general task of extracting paraphrases for events rather than the much more specific scriptrelated task.

Extends

acl_arc_intent_classification:train:1047

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: While IA is generally thought to be consistent with findings on human language production (Hermann and Deutsch 1976; Levelt 1989; Pechmann 1989; Sonnenschein 1982), the hypothesis that incrementality is a good model of human GRE seems unfalsifiable until a preference order is specified for the properties on which it operates. Citation Sentence: While IA is generally thought to be consistent with findings on human language production ( Hermann and Deutsch 1976 ; Levelt 1989 ; Pechmann 1989 ; Sonnenschein 1982 ) , the hypothesis that incrementality is a good model of human GRE seems unfalsifiable until a preference order is specified for the properties on which it operates . Context after the citation: (Wildly redundant descriptions can result if the ‘wrong’ preference order are chosen.) We shall see that vague descriptions pose particular challenges to incrementality. One question emerges when the IA is combined with findings on word order and incremental interpretation. If human speakers and/or writers perform CD incrementally, then why are properties not expressed in the same order in which they were selected?

Background

acl_arc_intent_classification:train:1048

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Typical examples are Bulgarian (Simov et al., 2005; Simov and Osenova, 2003), Chinese (Chen et al., 2003), Danish (Kromann, 2003), and Swedish (Nilsson et al., 2005). If we start by considering languages with a labeled attachment score of 85% or higher, they are characterized by high precision and recall for root nodes, typically 95/90, and by a graceful degradation of attachment score as arcs grow longer, typically 95–90–85, for arcs of length 1, 2 and 3–6. before we turn to Swedish and Turkish, focusing on recall and precision of root nodes, as a reflection of global syntactic structure, and on attachment score as a function of arc length. Citation Sentence: Typical examples are Bulgarian ( Simov et al. , 2005 ; Simov and Osenova , 2003 ) , Chinese ( Chen et al. , 2003 ) , Danish ( Kromann , 2003 ) , and Swedish ( Nilsson et al. , 2005 ) . Context after the citation: Japanese (Kawata and Bartels, 2000), despite a very high accuracy, is different in that attachment score drops from 98% to 85%, as we go from length 1 to 2, which may have something to do with the data consisting of transcribed speech with very short utterances. A second observation is that a high proportion of non-projective structures leads to fragmentation in the parser output, reflected in lower precision for roots. This is noticeable for German (Brants et al., 2002) and Portuguese (Afonso et al., 2002), which still have high overall accuracy thanks to very high attachment scores, but much more conspicuous for Czech (B¨ohmov´a et al., 2003), Dutch (van der Beek et al., 2002) and Slovene (Dˇzeroski et al., 2006), where root precision drops more drastically to about 69%, 71% and 41%, respectively, and root recall is also affected negatively. On the other hand, all three languages behave like high-accuracy languages with respect to attachment score.

Background

acl_arc_intent_classification:train:1049

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: TNT refers to the HPSG parser (Torisawa et al., 2000), C++ implementation of the two-phase parsing algorithm that performs filtering with a compiled CFG (phase 1) and then executes feature unification (phase 2). In Table 2, lem refers to the LTAG parser (Sarkar et al., 2000), ANSI C implementation of the two-phase parsing algorithm that performs the head corner parsing (van Noord, 1994) without features (phase 1), and then executes feature unification (phase 2). Table 2 shows the average parsing time with the LTAG and HPSG parsers. Citation Sentence: TNT refers to the HPSG parser ( Torisawa et al. , 2000 ) , C++ implementation of the two-phase parsing algorithm that performs filtering with a compiled CFG ( phase 1 ) and then executes feature unification ( phase 2 ) . Context after the citation: Table 2 clearly shows that the HPSG parser is significantly faster than the LTAG parser. This result implies that parsing techniques for HPSG are also beneficial for LTAG 4We eliminated 32 elementary trees because the LTAG parser cannot produce correct derivation trees with them. 5These lexical items are a subset of the original XTAG English grammar distribution.

CompareOrContrast

acl_arc_intent_classification:train:105

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: • A user study was performed, but it was either very small compared to the corpus (Carmel, Shtalhaim, and Soffer 2000; Jijkoun and de Rijke 2005), or the corpus itself was significantly smaller than ours (Feng et al. 2006; Leuski et al. 2006). • Only an automatic evaluation was performed, which relied on having model responses (Berger and Mittal 2000; Berger et al. 2000). • Only qualitative observations of the responses were reported (no formal evaluation was performed) (Lapalme and Kosseim 2003; Roy and Subramaniam 2006). Citation Sentence: • A user study was performed , but it was either very small compared to the corpus ( Carmel , Shtalhaim , and Soffer 2000 ; Jijkoun and de Rijke 2005 ) , or the corpus itself was significantly smaller than ours ( Feng et al. 2006 ; Leuski et al. 2006 ) . Context after the citation: The representativeness of the sample size was not discussed in any of these studies. There are significant practical difficulties associated with conducting the user studies needed to produce meaningful results for our system. Firstly, the size of our corpus and the number of parameters and settings that we need to test mean that in order for a user study to be representative, a fairly large sample involving several hundreds of request–response pairs would have to be used. Further, user-based evaluations of the output produced by our system require the subjects to read relatively long request– response e-mails, which quickly becomes tedious.

CompareOrContrast

acl_arc_intent_classification:train:1050

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Our work on the prosodic phrase status of clause final prepositional phrases, which we discuss below, suggests the existence of a discourse-neutral phrasing that depends on syntactic constituency mediated by string adjacency and length of a potential prosodic phrase.3 Such phrasing provides us with a typical phrasing pattern analogous to the typical phrasal stress patterns examined in Liberman and Prince (1977), which &quot;are often overwhelmed by the chiaroscuro of highlight and background in discourse, but retain the status of null-hypothesis patterns that emerge when Computational Linguistics Volume 16, Number 3, September 1990 157 J. Bachenko and E. Fitzpatrick Discourse-Neutral Prosodic Phrasing in English there is no good reason to take some other option&quot; (p. 251). In other words, when prosodic features that reflect facts of the discourse are removed, is there a residual, neutral phrasing? Thus both of these efforts leave open the question as to whether discourse features completely determine prosodic phrasing or are a complement to some more basic set of determinants, syntactic and/or phonological. Citation Sentence: Our work on the prosodic phrase status of clause final prepositional phrases , which we discuss below , suggests the existence of a discourse-neutral phrasing that depends on syntactic constituency mediated by string adjacency and length of a potential prosodic phrase .3 Such phrasing provides us with a typical phrasing pattern analogous to the typical phrasal stress patterns examined in Liberman and Prince ( 1977 ) , which `` are often overwhelmed by the chiaroscuro of highlight and background in discourse , but retain the status of null-hypothesis patterns that emerge when Computational Linguistics Volume 16 , Number 3 , September 1990 157 J. Bachenko and E. Fitzpatrick Discourse-Neutral Prosodic Phrasing in English there is no good reason to take some other option '' ( p. 251 ) . Context after the citation: This approach to prosodic phrase boundary determination brings us closer to a framework in which phonological, syntactic, and discourse features all contribute to prosodic phrasing. The possibility of a discourse-neutral prosodic phrasing is also of import to the prosodic quality of synthetic speech, since it allows us to &quot;get by&quot; without a complete description of the discourse features of a given text, many of which have yet to be characterized. Interestingly, in the data we examined we found only 14 percent of the phrases to be discourse-determined. The identification of a preferred phrasing that is independent of discourse also aids us in identifying and characterizing the discourse features that impinge on prosodic phrasing.

CompareOrContrast

acl_arc_intent_classification:train:1051

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information (Andrews et al., 2009; Steyvers, 2010; Feng and Lapata, 2010b; Bruni et al., 2011; Silberer and Lapata, 2012; Johns and Jones, 2012; Bruni et al., 2012a; Bruni et al., 2012b; Silberer et al., 2013). Some efforts have tackled tasks such as automatic image caption generation (Feng and Lapata, 2010a; Ordonez et al., 2011), text illustration (Joshi et al., 2006), or automatic location identification of Twitter users (Eisenstein et al., 2010; Wing and Baldridge, 2011; Roller et al., 2012). Others provide automatic mappings of natural language instructions to executable actions, such as interpreting navigation directions (Chen and Mooney, 2011) or robot commands (Tellex et al., 2011; Matuszek et al., 2012). Citation Sentence: Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information ( Andrews et al. , 2009 ; Steyvers , 2010 ; Feng and Lapata , 2010b ; Bruni et al. , 2011 ; Silberer and Lapata , 2012 ; Johns and Jones , 2012 ; Bruni et al. , 2012a ; Bruni et al. , 2012b ; Silberer et al. , 2013 ) . Context after the citation: Although these approaches have differed in model definition, the general goal in this line of research has been to enhance word meaning with perceptual information in order to address one of the most common criticisms of distributional semantics: that the “meaning of words is entirely given by other words” (Bruni et al., 2012b). In this paper, we explore various ways to integrate new perceptual information through novel computational modeling of this grounded knowledge into a multimodal distributional model of word meaning. The model we rely on was originally developed by Andrews et al. (2009) and is based on a generalization of Latent Dirichlet Allocation.

Background

acl_arc_intent_classification:train:1052

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: When objects are compared in terms of several dimensions, these dimensions can be weighed in different ways (e.g., Rasmusen 1989). 9.3.1 Combinations of Adjectives. Citation Sentence: When objects are compared in terms of several dimensions , these dimensions can be weighed in different ways ( e.g. , Rasmusen 1989 ) . Context after the citation: Let us focus on references to an individual referent r, starting with a description that contains more than one gradable adjective. The NP the tall fat giraffe, for example, can safely refer to an element b in a situation like the one below, where b is the only element that exceeds all distractors with respect to some dimension (a different one for a than for c, as it happens) while not being exceeded by any distractors in any dimension: Cases like this would be covered if the decision-theoretic property of Pareto optimality (e.g., Feldman 1980) was used as the sole criterion: Formally, an object r E C has a Pareto-optimal combination of Values V iff there is no other x E C such that 1. ]

Background

acl_arc_intent_classification:train:1053

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Most DOP models, such as in Bod (1993), Goodman (1996), Bonnema et al. (1997), Sima'an (2000) and Collins & Duffy (2002), use a likelihood criterion in defining the best parse tree: they take (some notion of) the most likely (i.e. most probable) tree as a candidate for the best tree of a sentence. Citation Sentence: Most DOP models , such as in Bod ( 1993 ) , Goodman ( 1996 ) , Bonnema et al. ( 1997 ) , Sima'an ( 2000 ) and Collins & Duffy ( 2002 ) , use a likelihood criterion in defining the best parse tree : they take ( some notion of ) the most likely ( i.e. most probable ) tree as a candidate for the best tree of a sentence . Context after the citation: We will refer to these models as Likelihood-DOP models, but in this paper we will specifically mean by &quot;Likelihood-DOP&quot; the PCFG-reduction of Bod (2001) given in Section 2.2. In Bod (2000b), an alternative notion for the best parse tree was proposed based on a simplicity criterion: instead of producing the most probable tree, this model produced the tree generated by the shortest derivation with the fewest training subtrees. We will refer to this model as Simplicity-DOP. In case the shortest derivation is not unique, Bod (2000b) proposes to back off to a frequency ordering of the subtrees.

Background

acl_arc_intent_classification:train:1054

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: While IA is generally thought to be consistent with findings on human language production (Hermann and Deutsch 1976; Levelt 1989; Pechmann 1989; Sonnenschein 1982), the hypothesis that incrementality is a good model of human GRE seems unfalsifiable until a preference order is specified for the properties on which it operates. Citation Sentence: While IA is generally thought to be consistent with findings on human language production ( Hermann and Deutsch 1976 ; Levelt 1989 ; Pechmann 1989 ; Sonnenschein 1982 ) , the hypothesis that incrementality is a good model of human GRE seems unfalsifiable until a preference order is specified for the properties on which it operates . Context after the citation: (Wildly redundant descriptions can result if the ‘wrong’ preference order are chosen.) We shall see that vague descriptions pose particular challenges to incrementality. One question emerges when the IA is combined with findings on word order and incremental interpretation. If human speakers and/or writers perform CD incrementally, then why are properties not expressed in the same order in which they were selected?

Background

acl_arc_intent_classification:train:1055

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary, i.e., mouse Mouse Genome Database (MGD) [18], fly FlyBase [19], yeast Saccharomyces Genome Database (SGD) [20], rat – Rat Genome Database (RGD) [21], worm – WormBase [22], Human Nomenclature Database (HUGO) [23], Online Mendelian Inheritance in Man (OMIM) [24], and Enzyme Nomenclature Database (ECNUM) [25, 26]. The Semantic Network contains information about the types or categories (e.g., “Disease or Syndrome”, “Virus”) to which all META concepts have been assigned. The SPECIALIST lexicon contains syntactic information for many terms, component words, and English words, including verbs, which do not appear in the META. Citation Sentence: Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] . Context after the citation:

Uses

acl_arc_intent_classification:train:1056

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar (Yoshinaga and Miyao, 2001). This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar (FB-LTAG1) (Vijay-Shanker, 1987; Vijay-Shanker and Joshi, 1988) and Head-Driven Phrase Structure Grammar (HPSG) (Pollard and Sag, 1994) by a method of grammar conversion. Citation Sentence: The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar ( Yoshinaga and Miyao , 2001 ) . Context after the citation: Strong equivalence means that both grammars generate exactly equivalent parse results, and that we can share the LTAG grammars and lexicons in HPSG applications. Our system can reduce considerable workload to develop a huge resource (grammars and lexicons) from scratch. Our concern is, however, not limited to the sharing of grammars and lexicons. Strongly equivalent grammars enable the sharing of ideas developed in each formalism.

Background

acl_arc_intent_classification:train:1057

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The features can be easily obtained by modifying the TAT extraction algorithm described in (Liu et al., 2006). Note that for lexicalized tree, features do not include the information of sub-trees since there is no nonterminals. Furthermore, we also build MERS models for lexicalized and unlexicalized source trees. Citation Sentence: The features can be easily obtained by modifying the TAT extraction algorithm described in ( Liu et al. , 2006 ) . Context after the citation: When a TAT is extracted from a word-aligned, source-parsed parallel sentence, we just record the contextual features and the features of the sub-trees. Then we use the toolkit implemented by Zhang (2004) to train MERS models for the ambiguous source syntactic trees separately. We set the iteration number to 100 and Gaussian prior to 1.

Extends

acl_arc_intent_classification:train:1058

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Marinov and Hemming (2004) present preliminary work on the automatic extraction of subcategorization frames for Bulgarian from the BulTreeBank (Simov, Popova, and Osenova 2002). The system learns 137 subcategorization frames from 19,126 sentences for 914 verbs (those which occurred five times or more). This is achieved using three different hypothesis tests: BHT, log-likelihood ratio, and t-score. Citation Sentence: Marinov and Hemming ( 2004 ) present preliminary work on the automatic extraction of subcategorization frames for Bulgarian from the BulTreeBank ( Simov , Popova , and Osenova 2002 ) . Context after the citation: In a similar way to that of Sarkar and Zeman (2000), Marinov and Hemming’s system collects both arguments and adjuncts. It then uses the binomial log-likelihood ratio to filter incorrect frames. The BulTreebank trees are annotated with HPSG-typed feature structure information and thus contain more detail than the dependency trees. The work done for Bulgarian is small-scale, however, as Marinov and Hemming are working with a preliminary version of the treebank with 580 sentences.

Background

acl_arc_intent_classification:train:1059

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: 1° The body of a plan can be an action or sequence of actions, a goal or sequence 9 Moore and Paris also note that &quot;a generation system must maintain the kinds of information outlined by Grosz and Sidner&quot; (Moore and Paris 1989, 203). Each matching plan's preconditions are checked; if they are currently (believed to be) true, the planner then attempts to find all instantiations of the plan's body. The planner first checks all of its top-level plans to see which have effects that match the goal. Citation Sentence: 1 ° The body of a plan can be an action or sequence of actions , a goal or sequence 9 Moore and Paris also note that `` a generation system must maintain the kinds of information outlined by Grosz and Sidner '' ( Moore and Paris 1989 , 203 ) . Context after the citation: Their planner uses plan structures similar to IGEN's, except that the plan operators they use are generally instantiations of rhetorical relations drawn from Rhetorical Structure Theory (Mann and Thompson 1987). In IGEN, the plans can involve any goals or actions that could be achieved via communication. Hovy has described another text planner that builds similar plans (Hovy 1988b). This system, however, starts with a list of information to be expressed and merely arranges it into a coherent pattern; it is thus not a planner in the sense used here (as Hovy makes clear).

Background

acl_arc_intent_classification:train:106

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The use of the web as a corpus for teaching and research on language has been proposed a number of times (Kilgarriff, 2001; Robb, 2003; Rundell, 2000; Fletcher, 2001, 2004b) and received a special issue of the journal Computational Linguistics (Kilgarriff and Grefenstette, 2003). This corpus annotation bottleneck becomes even more problematic for voluminous data sets drawn from the web. Larger systems to support multiple document tagging processes would require resources that cannot be realistically provided by existing single-server systems. Citation Sentence: The use of the web as a corpus for teaching and research on language has been proposed a number of times ( Kilgarriff , 2001 ; Robb , 2003 ; Rundell , 2000 ; Fletcher , 2001 , 2004b ) and received a special issue of the journal Computational Linguistics ( Kilgarriff and Grefenstette , 2003 ) . Context after the citation: Studies have used several different methods to mine web data. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguist’s Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003).

Background

acl_arc_intent_classification:train:1060

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: ASARES is based on a Machine Learning technique, Inductive Logic Programming (ILP) (Muggleton and De-Raedt, 1994), which infers general morpho-syntactic patterns from a set of examples (this set is noted E+ hereafter) and counter-examples (E−) of the elements one We simply give a short account of its basic principles herein. ASARES is presented in detail in (Claveau et al., 2003). Citation Sentence: ASARES is based on a Machine Learning technique , Inductive Logic Programming ( ILP ) ( Muggleton and De-Raedt , 1994 ) , which infers general morpho-syntactic patterns from a set of examples ( this set is noted E + hereafter ) and counter-examples ( E − ) of the elements one Context after the citation: 2CORDIAL is a commercial product of SynapseDeveloppement. wants to acquire and their context. The contextual patterns produced can then be applied to the corpus in order to retrieve new elements. The acquisition process can be summarized in 3 steps:

Background

acl_arc_intent_classification:train:1061

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Baroni and Bernardini (2004) built a corpus by iteratively searching Google for a small set of seed terms. Turney (2001) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler. Studies have used several different methods to mine web data. Citation Sentence: Baroni and Bernardini ( 2004 ) built a corpus by iteratively searching Google for a small set of seed terms . Context after the citation: Prototypes of Internet search engines for linguists, corpus linguists and lexicographers have been proposed: WebCorp (Kehoe and Renouf, 2002), KWiCFinder (Fletcher, 2004a) and the Linguist’s Search Engine (Kilgarriff, 2003; Resnik and Elkiss, 2003). A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies. Word frequency counts in internet search engines are inconsistent and unreliable (Veronis, 2005). Tools based on static corpora do not suffer from this problem, e.g. BNCweb7, developed at the University of Zurich, and View 8 (Variation in English Words and Phrases, developed at Brigham Young University)

Background

acl_arc_intent_classification:train:1062

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Expanding on a suggestion of Michiels (1982), we classify verbs as Subject Equi, Object Equi, Subject Raising or Object Raising for each sense which has a predicate complement code associated with it. In these situations the parser can use the semantic type of the verb to compute this relationship. In any subcategorisation frame which involves a predicate complement there will be a non-transparent relationship between the superficial syntactic form and the underlying logical relations in the sentence. Citation Sentence: Expanding on a suggestion of Michiels ( 1982 ) , we classify verbs as Subject Equi , Object Equi , Subject Raising or Object Raising for each sense which has a predicate complement code associated with it . Context after the citation: These terms, which derive from Transformational Grammar, are used as convenient labels for what we regard as a semantic distinction; the actual output of the program is a specification of the mapping from superficial syntactic form to an underlying logical representation. For example, labelling believe(3) (Type 2 ORaising) indicates that this is a two place predicate and that, if believe(3) occurs with a syntactic direct object, as in (1) John believes the Earth to be round it will function as the logical subject of the predicate complement.

Extends

acl_arc_intent_classification:train:1063

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This Principle of Finitism is also assumed by Johnson-Laird (1983), Jackendoff (1983), Kamp (1981), and implicitly or explicitly by almost all researchers in computational linguistics. The issues of control are not so important for us at this point; we restrict ourselves to describing the logic. Therefore these notions, and all other constructs we are going to define (axioms, metarules, definitions etc.) are computational, although usually we will not provide explicit algorithms for computing them. Citation Sentence: This Principle of Finitism is also assumed by Johnson-Laird ( 1983 ) , Jackendoff ( 1983 ) , Kamp ( 1981 ) , and implicitly or explicitly by almost all researchers in computational linguistics . Context after the citation: As a logical postulate it is not very radical; it is possible within a finitary framework to develop that part of mathematics that is used or has potential applications in natural science, such as mathematical analysis (cfXXX Mycielski 1981). On the other hand, a possible obstacle to our strategy of using only finite objects is the fact that the deductive closure of any set of formulas is not finite in standard logic, while, clearly, we will have to deduce new facts from formal representations of text and background knowledge. But there are several ways to avoid this obstruction. For example, consider theories consisting of universal formulas without function symbols.

CompareOrContrast

acl_arc_intent_classification:train:1064

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: For more information on CATiB, see Habash and Roth (2009) and Habash, Faraj, and Roth (2009). For the corpus statistics, see Table 1. An example CATiB dependency tree is shown in Figure 1. Citation Sentence: For more information on CATiB , see Habash and Roth ( 2009 ) and Habash , Faraj , and Roth ( 2009 ) . Context after the citation:

Background

acl_arc_intent_classification:train:1065

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: With the exception of (Fung, 1995b), previous methods for automatically constructing statistical translation models begin by looking at word cooccurrence frequencies in bitexts (Gale & Church, 1991; Kumano & Hirakawa, 1994; Fung, 1995a; Melamed, 1995). 2 Co-occurrence The hidden parameters can be conditioned on prior knowledge about the bitext to improve the model's accuracy. Citation Sentence: With the exception of ( Fung , 1995b ) , previous methods for automatically constructing statistical translation models begin by looking at word cooccurrence frequencies in bitexts ( Gale & Church , 1991 ; Kumano & Hirakawa , 1994 ; Fung , 1995a ; Melamed , 1995 ) . Context after the citation: A bitext comprises a pair of texts in two languages, where each text is a translation of the other. Word co-occurrence can be defined in various ways. The most common way is to divide each half of the bitext into an equal number of segments and to align the segments so that each pair of segments Si and Ti are translations of each other (Gale & Church, 1991; Melamed, 1996a). Then, two word tokens (u, v) are said to co-occur in the

Background

acl_arc_intent_classification:train:1066

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Experiments (Section 5) show that forestbased extraction improves BLEU score by over 1 point on a state-of-the-art tree-to-string system (Liu et al., 2006; Mi et al., 2008), which is also 0.5 points better than (and twice as fast as) extracting on 30-best parses. We instead propose a novel approach that extracts rules from packed forests (Section 3), which compactly encodes many more alternatives than kbest lists. also inefficient to extract rules separately from each of these very similar trees (or from the cross-product of k2 similar tree-pairs in tree-to-tree models). Citation Sentence: Experiments ( Section 5 ) show that forestbased extraction improves BLEU score by over 1 point on a state-of-the-art tree-to-string system ( Liu et al. , 2006 ; Mi et al. , 2008 ) , which is also 0.5 points better than ( and twice as fast as ) extracting on 30-best parses . Context after the citation: When combined with our previous orthogonal work on forest-based decoding (Mi et al., 2008), the forest-forest approach achieves a 2.5 BLEU points improvement over the baseline, and even outperforms the hierarchical system of Hiero, one of the best-performing systems to date. Besides tree-to-string systems, our method is also applicable to other paradigms such as the string-totree models (Galley et al., 2006) where the rules are in the reverse order, and easily generalizable to pairs of forests in tree-to-tree models. 2 Tree-based Translation We review in this section the tree-based approach to machine translation (Liu et al., 2006; Huang et al., 2006), and its rule extraction algorithm (Galley et al., 2004; Galley et al., 2006).

Extends

acl_arc_intent_classification:train:1067

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Our approach to extract and classify social events builds on our previous work (Agarwal and Rambow, 2010), which in turn builds on work from the relation extraction community (Nguyen et al., 2009). Take your own.” (6) “Take it,” said Emma, smiling, and pushing the paper towards Harriet “it is for you. Citation Sentence: Our approach to extract and classify social events builds on our previous work ( Agarwal and Rambow , 2010 ) , which in turn builds on work from the relation extraction community ( Nguyen et al. , 2009 ) . Context after the citation: Therefore, the task of relation extraction is most closely related to the tasks addressed in this paper. Researchers have used other notions of semantics in the literature such as latent semantic analysis (Plank and Moschitti, 2013) and relation-specific semantics (Zelenko et al., 2003; Culotta and Sorensen, 2004). To the best of our knowledge, there is only one work that uses frame semantics for relation extraction (Harabagiu et al., 2005). Harabagiu et al. (2005) propose a novel semantic kernel that incorporates frame parse information in the kernel computation that calculates similarity between two dependency trees.

Extends

acl_arc_intent_classification:train:1068

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests and satisfactions thereof) to classify messages, and thus also explicitly exploit the structure of conversations. We currently do not have an efficient means to encode disagreement information as hard constraints; we plan to investigate incorporating such information in future work. Citation Sentence: Previous sentiment-analysis work in different domains has considered inter-document similarity ( Agarwal and Bhattacharyya , 2005 ; Pang and Lee , 2005 ; Goldberg and Zhu , 2006 ) or explicit Context after the citation: inter-document references in the form of hyperlinks (Agrawal et al., 2003). Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). Zhu (2005) maintains a survey of this area. Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004).

Background

acl_arc_intent_classification:train:1069

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Surveys and articles on the topic include Lamarche and Retord (1996), de Groote and Retord (1996), and Morrill (1999). (Cut-free) proof nets exhibit no spurious ambiguity and play the role in categorial grammar that parse trees play in phrase structure grammar. Here we are concerned with categorial proof nets (Roorda 1991) as the fundamental structures of proof in categorial logic, in the same sense that linear proof nets were originally introduced by Girard (1987) as the fundamental structures of proof in linear logic. Citation Sentence: Surveys and articles on the topic include Lamarche and Retord ( 1996 ) , de Groote and Retord ( 1996 ) , and Morrill ( 1999 ) . Context after the citation: Still, at the risk of proceeding at a slightly slower pace, we aim nonetheless to include here enough details to make the present paper self-contained. A polar category formula is a Lambek categorial type labeled with input (') or output (°) polarity. A polar category formula tree is a binary ordered tree in which the leaves are labeled with polar atoms (literals) and each local tree is one of the following (logical) links: Without polarities, a formula tree is a kind of formation tree of the formula at its root:

Background

acl_arc_intent_classification:train:107

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: For example, speech repairs, particle omission, and fillers can be dealt with in the framework of unification grammar (Nakano et al., 1994; Nakano and Shimazu, 1999). Since the language understanding module utilizes unification, a wide variety of linguistic phenomena can be covered. Recording some dialogue history is also possible. Citation Sentence: For example , speech repairs , particle omission , and fillers can be dealt with in the framework of unification grammar ( Nakano et al. , 1994 ; Nakano and Shimazu , 1999 ) . Context after the citation: The language generation module features Common Lisp functions, so there is no limitation on the description. Some of the systems we have developed feature a generation method based on hierarchical planning (Dohsaka and Shimazu, 1997). It is also possible to build a simple finite-state-model-based dialogue system using WIT. States can be represented by dialogue phases in WIT.

FutureWork

acl_arc_intent_classification:train:1070

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Levenberg et al. (2012) employed a Bayesian method to learn discontinuous SCFG rules. The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system (Chiang, 2007). Blunsom et al. (2008, 2009, 2010) utilized Bayesian methods to learn synchronous context free grammars (SCFG) from a parallel corpus. Citation Sentence: Levenberg et al. ( 2012 ) employed a Bayesian method to learn discontinuous SCFG rules . Context after the citation: This study differs from their work because we concentrate on constructing tree structures for tree-based translation models. Our U-trees are learned based on STSG, which is more appropriate for tree-based translation models than SCFG. Burkett and Klein (2008) and Burkett et al. (2010) focused on joint parsing and alignment. They utilized the bilingual Tree-bank to train a joint model for both parsing and word alignment.

CompareOrContrast

acl_arc_intent_classification:train:1071

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Table 5 shows our mapping from publication type and MeSH headings to evidence grades based on principles defined in the Strength of Recommendations Taxonomy (Ebell et al. 2004). The potential highest level of the strength of evidence for a given citation can be identified using the Publication Type (a metadata field) and MeSH terms pertaining to the type of the clinical study. Metadata associated with most MEDLINE citations (MeSH terms) are extensively used to determine the strength of evidence and in our EBM citation scoring algorithm (Section 6). Citation Sentence: Table 5 shows our mapping from publication type and MeSH headings to evidence grades based on principles defined in the Strength of Recommendations Taxonomy ( Ebell et al. 2004 ) . Context after the citation: 5.10 Sample Output A complete example of our knowledge extractors working in unison is shown in Figure 2, which contains an abstract retrieved in response to the following question: “In children with an acute febrile illness, what is the efficacy of single-medication therapy with acetaminophen or ibuprofen in reducing fever?” (Kauffman, Sawyer, and Scheinbaum 1992). Febrile illness is the only concept mapped to DISORDER, and hence is identified as the primary problem.

Uses

acl_arc_intent_classification:train:1072

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: We use the same splits as Garrette et al. (2014). Each corpus was divided into four distinct data sets: a set from which we extract the tag dictionaries, a set of raw (unannotated) sentences, a development set, and a test set. We evaluated on the English CCGBank (Hockenmaier and Steedman, 2007), which is a transformation of the Penn Treebank (Marcus et al., 1993); the CTBCCG (Tse and Curran, 2010) transformation of the Penn Chinese Treebank (Xue et al., 2005); and the CCG-TUT corpus (Bos et al., 2009), built from the TUT corpus of Italian text (Bosco et al., 2000). Citation Sentence: We use the same splits as Garrette et al. ( 2014 ) . Context after the citation: Since these treebanks use special representations for conjunctions, we chose to rewrite the trees to use conjunction categories of the form (X\X)/X rather than introducing special conjunction rules. In order to increase the amount of raw data available to the sampler, we supplemented the English data with raw, unannotated newswire sentences from the NYT Gigaword 5 corpus (Parker et al., 2011) and supplemented Italian with the out-of-domain WaCky corpus (Baroni et al., 1999). For English and Italian, this allowed us to use 100k raw tokens for training (Chinese uses 62k). For Chinese and Italian, for training efficiency, we used only raw sentences that were 50 words or fewer (note that we did not drop tag dictionary set or test set sentences).

Uses

acl_arc_intent_classification:train:1073

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: These results are slightly worse than those obtained in previous studies using the same annotation scheme (Jokinen et al., 2008), but are still sat- feature Cohen’s k corrected k face segment 69.89 91.37 face annotate 71.53 94.25 head mov segment 71.21 91.75 head mov annotate 71.65 95.14 The figures obtained are given in Table 3. Citation Sentence: These results are slightly worse than those obtained in previous studies using the same annotation scheme ( Jokinen et al. , 2008 ) , but are still sat - Context after the citation: isfactory given the high number of categories provided by the scheme. A distinction that seemed particularly difficult was that between nods and jerks: although the direction of the two movement types is different (down-up and up-down, respectively), the movement quality is very similar, and makes it difficult to see the direction clearly. We return to this point below, in connection with our data analysis. 5 Analysis of the data

CompareOrContrast

acl_arc_intent_classification:train:1074

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In Chomsky and Halle (1968), this flattening process is not part of the grammar. The result is a flattened structure that more accurately reflects the prosodic phrasing. To account for such mismatches, &quot;readjustment rules&quot; that change constituent structure by adjoining each embedded sentence to the node dominating it have been posited. Citation Sentence: In Chomsky and Halle ( 1968 ) , this flattening process is not part of the grammar . Context after the citation: Rather, it is viewed as &quot;. . a performance factor, related to the difficulty of producing right branching structures such as [12]&quot; (p. 372). Thus phrasing, in their approach, is only indirectly related to syntax, since readjustment is done by special rules outside the grammar proper.

Background

acl_arc_intent_classification:train:1075

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: MEDLINE, the authoritative repository of abstracts from the medical and biomedical primary literature maintained by the National Library of Medicine, provides the clinically relevant sources for answering physicians’ questions, and is commonly used in that capacity (Cogdill and Moore 1997; De Groote and Dorsch 2003). Furthermore, the need to answer questions related to patient care at the point of service has been well studied and documented (Covell, Uman, and Manning 1985; Gorman, Ash, and Wykoff 1994; Ely et al. 1999, 2005). The confluence of these many factors makes clinical question answering a very exciting area of research. Citation Sentence: MEDLINE , the authoritative repository of abstracts from the medical and biomedical primary literature maintained by the National Library of Medicine , provides the clinically relevant sources for answering physicians ' questions , and is commonly used in that capacity ( Cogdill and Moore 1997 ; De Groote and Dorsch 2003 ) . Context after the citation: However, studies have shown that existing systems for searching MEDLINE (such as PubMed, the search service provided by the National Library of Medicine) are often inadequate and unable to supply clinically relevant answers in a timely manner (Gorman, Ash, and Wykoff 1994; Chambliss and Conley 1996). Furthermore, it is clear that traditional document retrieval technology applied to MEDLINE abstracts is insufficient for satisfactory information access; research and experience point to the need for systems that automatically analyze text and return only the relevant information, appropriately summarizing and fusing segments from multiple texts. Not only is clinical question answering interesting from a research perspective, it also represents a potentially high-impact, real-world application of language processing and information retrieval technology—better information systems to provide decision support for physicians have the potential to improve the quality of health care. Our question-answering system supports the practice of evidence-based medicine (EBM), a widely accepted paradigm for medical practice that stresses the importance of evidence from patient-centered clinical research in the health care process.

Background

acl_arc_intent_classification:train:1076

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: There is a rich literature on organization and lexical access of morphologically complex words where experiments have been conducted mainly for derivational suffixed words of English, Hebrew, Italian, French, Dutch, and few other languages (Marslen-Wilson et al., 2008; Frost et al., 1997; Grainger, et al., 1991; Drews and Zwitserlood, 1995). Their computational significance arises from the issue of their storage in lexical resources like WordNet (Fellbaum, 1998) and raises the questions like, how to store morphologically complex words, in a lexical resource like WordNet keeping in mind the storage and access efficiency. Further, these linguistically important and interesting questions are also highly significant for computational linguistics (CL) and natural language processing (NLP) applications. Citation Sentence: There is a rich literature on organization and lexical access of morphologically complex words where experiments have been conducted mainly for derivational suffixed words of English , Hebrew , Italian , French , Dutch , and few other languages ( Marslen-Wilson et al. , 2008 ; Frost et al. , 1997 ; Grainger , et al. , 1991 ; Drews and Zwitserlood , 1995 ) . Context after the citation: However, we do not know of any such investigations for Indian languages, which are morphologically richer than many of their Indo-European cousins. Moreover, Indian languages show some distinct phenomena like, compound and composite verbs for which no such investigations have been conducted yet. On the other hand, experiments indicate that mental representation and processing of morphologically complex words are not quite language independent (Taft, 2004).

Background

acl_arc_intent_classification:train:1077

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: He lists, classifies, and discusses various types of inference, by which he means, generally, &quot;the linguistic-logical notions of consequent and presupposition&quot; Crothers (1979:112) have collected convincing evidence of the existence of language chunks—real structures, not just orthographic conventions—that are smaller than a discourse, larger than a sentence, generally composed of sentences, and recursive in nature (like sentences). His work is taxonomic, in that he performs detailed descriptive analyses of paragraphs. The textualist approach to paragraph analysis is exemplified by E. J. Crothers. Citation Sentence: He lists , classifies , and discusses various types of inference , by which he means , generally , `` the linguistic-logical notions of consequent and presupposition '' Crothers ( 1979:112 ) have collected convincing evidence of the existence of language chunks -- real structures , not just orthographic conventions -- that are smaller than a discourse , larger than a sentence , generally composed of sentences , and recursive in nature ( like sentences ) . Context after the citation: These chunks are sometimes called &quot;episodes,&quot; and sometimes &quot;paragraphs.&quot; According to Hinds (1979), paragraphs are made up of segments, which in turn are made up of sentences or clauses, which in turn are made up of phrases. Paragraphs therefore give hierarchical structure to sentences. Hinds discusses three major types of paragraphs, and their corresponding segment types.

Background

acl_arc_intent_classification:train:1078

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For this research, we used a coreference resolution system ((Harabagiu and Maiorano, 1999)) that implements different sets of heuristics corresponding to various forms of coreference. For example, CogNIAC (Baldwin, 1997), a system based on seven ordered heuristics, generates high-precision resolution (over 90%) for some cases of pronominal reference. Nevertheless, recent results show that knowledge-poor methods perform with amazing accuracy (cfXXX (Mitkov, 1998), (Kennedy and Boguraev, 1996) (Kameyama, 1997)). Citation Sentence: For this research , we used a coreference resolution system ( ( Harabagiu and Maiorano , 1999 ) ) that implements different sets of heuristics corresponding to various forms of coreference . Context after the citation: This system, called COCKTAIL, resolves coreference by exploiting several textual cohesion constraints (e.g. term repetition) combined with lexical and textual coherence cues (e.g. subjects of communication verbs are more likely to refer to the last person mentioned in the text). These constraints are implemented as a set of heuristics ordered by their priority. Moreover, the COCKTAIL framework uniformly addresses the problem of interaction between different forms of coreference, thus making the extension to multilingual coreference very natural.

Uses

acl_arc_intent_classification:train:1079

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Another line of research that is correlated with ours is recognition of agreement/disagreement (Misra and Walker, 2013; Yin et al., 2012; Abbott et al., 2011; Andreas et al., 2012; Galley et al., 2004; Hillard et al., 2003) and classification of stances (Walker et al., 2012; Somasundaran and Wiebe, 2010) in online forums. The corpus and guidelines will also be shared with the research community. Our work contributes a new principled method for building annotated corpora for online interactions. Citation Sentence: Another line of research that is correlated with ours is recognition of agreement/disagreement ( Misra and Walker , 2013 ; Yin et al. , 2012 ; Abbott et al. , 2011 ; Andreas et al. , 2012 ; Galley et al. , 2004 ; Hillard et al. , 2003 ) and classification of stances ( Walker et al. , 2012 ; Somasundaran and Wiebe , 2010 ) in online forums . Context after the citation: For future work, we can utilize textual features (contextual, dependency, discourse markers), relevant multiword expressions and topic modeling (Mukherjee and Liu, 2013), and thread structure (Murakami and Raymond, 2010; Agrawal et al., 2003) to improve the Agree/Disagree classification accuracy. Recently, Cabrio and Villata (2013) proposed a new direction of argumentative analysis where the authors show how arguments are associated with Recognizing Textual Entailment (RTE) research. They utilized RTE approach to detect the relation of support/attack among arguments (entailment expresses a ‘support’ and contradiction expresses an ‘attack’) on a dataset of arguments collected from online debates (e.g., Debatepedia).

CompareOrContrast

acl_arc_intent_classification:train:108

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The work that is most similar to ours is that of Chang et al. (2007), who introduced the Constraint Driven Learning algorithm (CODL). Their goal is considerably different; they want to incorporate additional features into their model and define an objective function which allows them to do so; whereas, we are interested in allowing for multiple objective functions in order to adapt the parser model parameters to downstream tasks or alternative intrinsic (parsing) objectives. In their work, they are interested in learning a parameterization over translation phrases (including the underlying wordalignment) which optimizes the BLEU score. Citation Sentence: The work that is most similar to ours is that of Chang et al. ( 2007 ) , who introduced the Constraint Driven Learning algorithm ( CODL ) . Context after the citation: Their algorithm specifically optimizes a loss function with the addition of constraints based on unlabeled data with a shared underlying parameter space. (what we call extrinsic datasets). For each unla6 Discussion beled example, they use the current model along The empirical results show that incorporating an with their set of constraints to select a set of k auaugmented-loss using the inline-ranker loss frametomatically labeled examples which best meet the work achieves better performance under metrics asconstraints.

CompareOrContrast

acl_arc_intent_classification:train:1080

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For example, in ordinary HMM training, xi = E* and represents a completely hidden state sequence (cfXXX Ristad (1998), who allows any regular set), while yi is a single string representing a completely observed emission sequence.11 What to optimize? Samples need not be fully observed (partly supervised training): thus xi C E*, yi C A* may be given as regular sets in which input and output were observed to fall. These are assumed to be independent random samples from a joint distribution of the form fe(x, y); the goal is to recover the true ˆ0. Citation Sentence: For example , in ordinary HMM training , xi = E * and represents a completely hidden state sequence ( cfXXX Ristad ( 1998 ) , who allows any regular set ) , while yi is a single string representing a completely observed emission sequence .11 What to optimize ? Context after the citation: Maximum-likelihood estimation guesses 0ˆ to be the 0 maximizing Hi fθ(xi, yi). Maximum-posterior estimation tries to maximize P(0)·Hi fθ(xi, yi) where P(0) is a prior probability. In a log-linear parameterization, for example, a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting (Chen and Rosenfeld, 1999). The EM algorithm (Dempster et al., 1977) can maximize these functions.

Background

acl_arc_intent_classification:train:1081

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Fraser (2009) tried to solve the inflection prediction problem by simply building an SMT system for translating from stems to inflected forms. We use more complex context features. Koehn and Hoang (2007) introduced factored SMT. Citation Sentence: Fraser ( 2009 ) tried to solve the inflection prediction problem by simply building an SMT system for translating from stems to inflected forms . Context after the citation: Bojar and Kos (2010) improved on this by marking prepositions with the case they mark (one of the most important markups in our system). Both efforts were ineffective on large data sets. Williams and Koehn (2011) used unification in an SMT system to model some of the agreement phenomena that we model.

CompareOrContrast

acl_arc_intent_classification:train:1082

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: As shown in (Okanohara and Tsujii, 2007), using this representation, a linear classifier cannot distinguish sentences sampled from a trigram and real sentences. This code was extensively optimized to take advantage of the very sparse sentence representation described above. The code for the classifier was generously provided by Daisuke Okanohara. Citation Sentence: As shown in ( Okanohara and Tsujii , 2007 ) , using this representation , a linear classifier can not distinguish sentences sampled from a trigram and real sentences . Context after the citation: Therefore, we used a 3rd order polynomial kernel, which was found to give good results. No special effort was otherwise made in order to optimize the parameters of the classifiers. 4. Stopping criterion: The process of adding features to the model was continued until the classification performance of the next feature was within 2% of chance performance.

Motivation

acl_arc_intent_classification:train:1083

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Various approaches for computing semantic relatedness of words or concepts have been proposed, e.g. dictionary-based (Lesk, 1986), ontology-based (Wu and Palmer, 1994; Leacock and Chodorow, 1998), information-based (Resnik, 1995; Jiang and Conrath, 1997) or distributional (Weeds and Weir, 2005). Citation Sentence: Various approaches for computing semantic relatedness of words or concepts have been proposed , e.g. dictionary-based ( Lesk , 1986 ) , ontology-based ( Wu and Palmer , 1994 ; Leacock and Chodorow , 1998 ) , information-based ( Resnik , 1995 ; Jiang and Conrath , 1997 ) or distributional ( Weeds and Weir , 2005 ) . Context after the citation: The knowledge sources used for computing relatedness can be as different as dictionaries, ontologies or large corpora. According to Budanitsky and Hirst (2006), there are three prevalent approaches for evaluating SR measures: mathematical analysis, applicationspecific evaluation and comparison with human judgments. Mathematical analysis can assess a measure with respect to some formal properties, e.g. whether a measure is a metric (Lin, 1998).4 However, mathematical analysis cannot tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application. The latter question is tackled by applicationspecific evaluation, where a measure is tested within the framework of a certain application, e.g. word sense disambiguation (Patwardhan et al., 2003) or malapropism detection (Budanitsky and Hirst, 2006).

Background

acl_arc_intent_classification:train:1084

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This framework, where the “semantic load” is spread more evenly throughout the lexicon to lexical categories not typically thought to bear semantic content, is essentially the model advocated by Pustejovsky (1991a), among many others. In this case, the complete event structure of a word can be compositionally derived from its component morphemes. (7) JflatK = [state flat] Citation Sentence: This framework , where the `` semantic load '' is spread more evenly throughout the lexicon to lexical categories not typically thought to bear semantic content , is essentially the model advocated by Pustejovsky ( 1991a ) , among many others . Context after the citation: Note that such an approach is no longer lexicalist: each lexical item does not fully encode its associated syntactic and semantic structures. Rather, meanings are composed from component morphemes. In addition to -en, other productive derivational suffixes in English such as -er, -ize, -ion, just to name a few, can be analyzed in a similar way. In fact, we may view morphological rules for composing morphemes into larger phonological units the same way we view syntactic rules for combining constituents into higher-level projections, i.e., why distinguish VP → V + NP from V → Adj + -en?

Background

acl_arc_intent_classification:train:1085

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Xia (1999) also presents a similar method for the extraction of a TAG from the Penn Treebank. The number of frame types extracted (i.e., an elementary tree without a specific lexical anchor) ranged from 2,366 to 8,996. This results in the construction of a set of lexically anchored elementary trees which make up the TAG in question. Citation Sentence: Xia ( 1999 ) also presents a similar method for the extraction of a TAG from the Penn Treebank . Context after the citation: The extraction procedure consists of three steps: First, the bracketing of the trees in the Penn Treebank is corrected and extended based on the approaches of Magerman (1994) and Collins (1997). Then the elementary trees are read off in a quite straightforward manner. Finally any invalid elementary trees produced as a result of annotation errors in the treebank are filtered out using linguistic heuristics. The number of frame types extracted by Xia (1999) ranged from 3,014 to 6,099.

Background

acl_arc_intent_classification:train:1086

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: To a first approximation, a CURRENT-FOCUS reaches only nodes that are c-commanded (Chomsky 1977) by its generator. That is to say, the CURRENT-FOCUS is a feature, like verb-mode, that is blocked when an [end] node is encountered. The CURRENT-FOCUS only gets passed along to siblings and their descendants, and hence is unavailable to activators at higher levels of the parse tree. Citation Sentence: To a first approximation , a CURRENT-FOCUS reaches only nodes that are c-commanded ( Chomsky 1977 ) by its generator . Context after the citation: Finally, certain blocker nodes block the transfer of the FLOAT-OBJECT to their children. A simple example will help explain how this works. For the sentence &quot;(How many pies), did Mike buy (t,)?&quot; as illustrated by the parse tree in Figure 3, the [qsubject] &quot;how many pies&quot; is a generator, so it fills the CURRENT-FOCUS with its subparse.

Background

acl_arc_intent_classification:train:1087