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{ "paragraphs": [ [ "Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive).", "Learning affective events is challenging because, as the examples above suggest, the polarity of an event is not necessarily predictable from its constituent words. Combined with the unbounded combinatorial nature of language, the non-compositionality of affective polarity entails the need for large amounts of world knowledge, which can hardly be learned from small annotated data.", "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.", "We trained the models using a Japanese web corpus. Given the minimum amount of supervision, they performed well. In addition, the combination of annotated and unannotated data yielded a gain over a purely supervised baseline when labeled data were small." ], [ "Learning affective events is closely related to sentiment analysis. Whereas sentiment analysis usually focuses on the polarity of what are described (e.g., movies), we work on how people are typically affected by events. In sentiment analysis, much attention has been paid to compositionality. Word-level polarity BIBREF5, BIBREF6, BIBREF7 and the roles of negation and intensification BIBREF8, BIBREF6, BIBREF9 are among the most important topics. In contrast, we are more interested in recognizing the sentiment polarity of an event that pertains to commonsense knowledge (e.g., getting money and catching cold).", "Label propagation from seed instances is a common approach to inducing sentiment polarities. While BIBREF5 and BIBREF10 worked on word- and phrase-level polarities, BIBREF0 dealt with event-level polarities. BIBREF5 and BIBREF10 linked instances using co-occurrence information and/or phrase-level coordinations (e.g., “$A$ and $B$” and “$A$ but $B$”). We shift our scope to event pairs that are more complex than phrase pairs, and consequently exploit discourse connectives as event-level counterparts of phrase-level conjunctions.", "BIBREF0 constructed a network of events using word embedding-derived similarities. Compared with this method, our discourse relation-based linking of events is much simpler and more intuitive.", "Some previous studies made use of document structure to understand the sentiment. BIBREF11 proposed a sentiment-specific pre-training strategy using unlabeled dialog data (tweet-reply pairs). BIBREF12 proposed a method of building a polarity-tagged corpus (ACP Corpus). They automatically gathered sentences that had positive or negative opinions utilizing HTML layout structures in addition to linguistic patterns. Our method depends only on raw texts and thus has wider applicability.", "" ], [ "" ], [ "", "Our goal is to learn the polarity function $p(x)$, which predicts the sentiment polarity score of an event $x$. We approximate $p(x)$ by a neural network with the following form:", "${\\rm Encoder}$ outputs a vector representation of the event $x$. ${\\rm Linear}$ is a fully-connected layer and transforms the representation into a scalar. ${\\rm tanh}$ is the hyperbolic tangent and transforms the scalar into a score ranging from $-1$ to 1. In Section SECREF21, we consider two specific implementations of ${\\rm Encoder}$.", "" ], [ "Our method requires a very small seed lexicon and a large raw corpus. We assume that we can automatically extract discourse-tagged event pairs, $(x_{i1}, x_{i2})$ ($i=1, \\cdots $) from the raw corpus. We refer to $x_{i1}$ and $x_{i2}$ as former and latter events, respectively. As shown in Figure FIGREF1, we limit our scope to two discourse relations: Cause and Concession.", "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types.", "" ], [ "The seed lexicon matches (1) the latter event but (2) not the former event, and (3) their discourse relation type is Cause or Concession. If the discourse relation type is Cause, the former event is given the same score as the latter. Likewise, if the discourse relation type is Concession, the former event is given the opposite of the latter's score. They are used as reference scores during training.", "" ], [ "The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Cause. We assume the two events have the same polarities.", "" ], [ "The seed lexicon matches neither the former nor the latter event, and their discourse relation type is Concession. We assume the two events have the reversed polarities.", "" ], [ "Using AL, CA, and CO data, we optimize the parameters of the polarity function $p(x)$. We define a loss function for each of the three types of event pairs and sum up the multiple loss functions.", "We use mean squared error to construct loss functions. For the AL data, the loss function is defined as:", "where $x_{i1}$ and $x_{i2}$ are the $i$-th pair of the AL data. $r_{i1}$ and $r_{i2}$ are the automatically-assigned scores of $x_{i1}$ and $x_{i2}$, respectively. $N_{\\rm AL}$ is the total number of AL pairs, and $\\lambda _{\\rm AL}$ is a hyperparameter.", "For the CA data, the loss function is defined as:", "$y_{i1}$ and $y_{i2}$ are the $i$-th pair of the CA pairs. $N_{\\rm CA}$ is the total number of CA pairs. $\\lambda _{\\rm CA}$ and $\\mu $ are hyperparameters. The first term makes the scores of the two events closer while the second term prevents the scores from shrinking to zero.", "The loss function for the CO data is defined analogously:", "The difference is that the first term makes the scores of the two events distant from each other.", "" ], [ "" ], [ "" ], [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.", ". 重大な失敗を犯したので、仕事をクビになった。", "Because [I] made a serious mistake, [I] got fired.", "From this sentence, we extracted the event pair of “重大な失敗を犯す” ([I] make a serious mistake) and “仕事をクビになる” ([I] get fired), and tagged it with Cause.", "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16." ], [ "We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well. Extracted from Japanese websites using HTML layouts and linguistic patterns, the dataset covered various genres. For example, the following two sentences were labeled positive and negative, respectively:", ". 作業が楽だ。", "The work is easy.", ". 駐車場がない。", "There is no parking lot.", "Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19.", "The objective function for supervised training is:", "", "where $v_i$ is the $i$-th event, $R_i$ is the reference score of $v_i$, and $N_{\\rm ACP}$ is the number of the events of the ACP Corpus.", "To optimize the hyperparameters, we used the dev set of the ACP Corpus. For the evaluation, we used the test set of the ACP Corpus. The model output was classified as positive if $p(x) > 0$ and negative if $p(x) \\le 0$.", "" ], [ "As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.", "BERT BIBREF17 is a pre-trained multi-layer bidirectional Transformer BIBREF18 encoder. Its output is the final hidden state corresponding to the special classification tag ([CLS]). For the details of ${\\rm Encoder}$, see Sections SECREF30.", "We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\\mathcal {L}_{\\rm AL}$, $\\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$, $\\mathcal {L}_{\\rm ACP}$, and $\\mathcal {L}_{\\rm ACP} + \\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$.", "" ], [ "", "Table TABREF23 shows accuracy. As the Random baseline suggests, positive and negative labels were distributed evenly. The Random+Seed baseline made use of the seed lexicon and output the corresponding label (or the reverse of it for negation) if the event's predicate is in the seed lexicon. We can see that the seed lexicon itself had practically no impact on prediction.", "The models in the top block performed considerably better than the random baselines. The performance gaps with their (semi-)supervised counterparts, shown in the middle block, were less than 7%. This demonstrates the effectiveness of discourse relation-based label propagation.", "Comparing the model variants, we obtained the highest score with the BiGRU encoder trained with the AL+CA+CO dataset. BERT was competitive but its performance went down if CA and CO were used in addition to AL. We conjecture that BERT was more sensitive to noises found more frequently in CA and CO.", "Contrary to our expectations, supervised models (ACP) outperformed semi-supervised models (ACP+AL+CA+CO). This suggests that the training set of 0.6 million events is sufficiently large for training the models. For comparison, we trained the models with a subset (6,000 events) of the ACP dataset. As the results shown in Table TABREF24 demonstrate, our method is effective when labeled data are small.", "The result of hyperparameter optimization for the BiGRU encoder was as follows:", "As the CA and CO pairs were equal in size (Table TABREF16), $\\lambda _{\\rm CA}$ and $\\lambda _{\\rm CO}$ were comparable values. $\\lambda _{\\rm CA}$ was about one-third of $\\lambda _{\\rm CO}$, and this indicated that the CA pairs were noisier than the CO pairs. A major type of CA pairs that violates our assumption was in the form of “$\\textit {problem}_{\\text{negative}}$ causes $\\textit {solution}_{\\text{positive}}$”:", ". (悪いところがある, よくなるように努力する)", "(there is a bad point, [I] try to improve [it])", "The polarities of the two events were reversed in spite of the Cause relation, and this lowered the value of $\\lambda _{\\rm CA}$.", "Some examples of model outputs are shown in Table TABREF26. The first two examples suggest that our model successfully learned negation without explicit supervision. Similarly, the next two examples differ only in voice but the model correctly recognized that they had opposite polarities. The last two examples share the predicate “落とす\" (drop) and only the objects are different. The second event “肩を落とす\" (lit. drop one's shoulders) is an idiom that expresses a disappointed feeling. The examples demonstrate that our model correctly learned non-compositional expressions.", "" ], [ "In this paper, we proposed to use discourse relations to effectively propagate polarities of affective events from seeds. Experiments show that, even with a minimal amount of supervision, the proposed method performed well.", "Although event pairs linked by discourse analysis are shown to be useful, they nevertheless contain noises. Adding linguistically-motivated filtering rules would help improve the performance." ], [ "We thank Nobuhiro Kaji for providing the ACP Corpus and Hirokazu Kiyomaru and Yudai Kishimoto for their help in extracting event pairs. This work was partially supported by Yahoo! Japan Corporation." ], [ "喜ぶ (rejoice), 嬉しい (be glad), 楽しい (be pleasant), 幸せ (be happy), 感動 (be impressed), 興奮 (be excited), 懐かしい (feel nostalgic), 好き (like), 尊敬 (respect), 安心 (be relieved), 感心 (admire), 落ち着く (be calm), 満足 (be satisfied), 癒される (be healed), and スッキリ (be refreshed)." ], [ "怒る (get angry), 悲しい (be sad), 寂しい (be lonely), 怖い (be scared), 不安 (feel anxious), 恥ずかしい (be embarrassed), 嫌 (hate), 落ち込む (feel down), 退屈 (be bored), 絶望 (feel hopeless), 辛い (have a hard time), 困る (have trouble), 憂鬱 (be depressed), 心配 (be worried), and 情けない (be sorry)." ], [ "The dimension of the embedding layer was 256. The embedding layer was initialized with the word embeddings pretrained using the Web corpus. The input sentences were segmented into words by the morphological analyzer Juman++. The vocabulary size was 100,000. The number of hidden layers was 2. The dimension of hidden units was 256. The optimizer was Momentum SGD BIBREF21. The mini-batch size was 1024. We ran 100 epochs and selected the snapshot that achieved the highest score for the dev set." ], [ "We used a Japanese BERT model pretrained with Japanese Wikipedia. The input sentences were segmented into words by Juman++, and words were broken into subwords by applying BPE BIBREF20. The vocabulary size was 32,000. The maximum length of an input sequence was 128. The number of hidden layers was 12. The dimension of hidden units was 768. The number of self-attention heads was 12. The optimizer was Adam BIBREF19. The mini-batch size was 32. We ran 1 epoch." ] ], "section_name": [ "Introduction", "Related Work", "Proposed Method", "Proposed Method ::: Polarity Function", "Proposed Method ::: Discourse Relation-Based Event Pairs", "Proposed Method ::: Discourse Relation-Based Event Pairs ::: AL (Automatically Labeled Pairs)", "Proposed Method ::: Discourse Relation-Based Event Pairs ::: CA (Cause Pairs)", "Proposed Method ::: Discourse Relation-Based Event Pairs ::: CO (Concession Pairs)", "Proposed Method ::: Loss Functions", "Experiments", "Experiments ::: Dataset", "Experiments ::: Dataset ::: AL, CA, and CO", "Experiments ::: Dataset ::: ACP (ACP Corpus)", "Experiments ::: Model Configurations", "Experiments ::: Results and Discussion", "Conclusion", "Acknowledgments", "Appendices ::: Seed Lexicon ::: Positive Words", "Appendices ::: Seed Lexicon ::: Negative Words", "Appendices ::: Settings of Encoder ::: BiGRU", "Appendices ::: Settings of Encoder ::: BERT" ] }

{ "answers": [ { "annotation_id": [ "31e85022a847f37c15fd0415f3c450c74c8e4755", "95da0a6e1b08db74a405c6a71067c9b272a50ff5" ], "answer": [ { "evidence": [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ], "extractive_spans": [], "free_form_answer": "a vocabulary of positive and negative predicates that helps determine the polarity score of an event", "highlighted_evidence": [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event.", "It is a " ], "unanswerable": false, "yes_no": null }, { "evidence": [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ], "extractive_spans": [ "seed lexicon consists of positive and negative predicates" ], "free_form_answer": "", "highlighted_evidence": [ "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "2cfd959e433f290bb50b55722370f0d22fe090b7" ] }, { "annotation_id": [ "1e5e867244ea656c4b7632628086209cf9bae5fa" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 3: Performance of various models on the ACP test set.", "FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data.", "As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. GRU BIBREF16 is a recurrent neural network sequence encoder. BiGRU reads an input sequence forward and backward and the output is the concatenation of the final forward and backward hidden states.", "We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\\mathcal {L}_{\\rm AL}$, $\\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$, $\\mathcal {L}_{\\rm ACP}$, and $\\mathcal {L}_{\\rm ACP} + \\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$." ], "extractive_spans": [], "free_form_answer": "Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. \nUsing a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO.", "highlighted_evidence": [ "FLOAT SELECTED: Table 3: Performance of various models on the ACP test set.", "FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data.", "As for ${\\rm Encoder}$, we compared two types of neural networks: BiGRU and BERT. ", "We trained the model with the following four combinations of the datasets: AL, AL+CA+CO (two proposed models), ACP (supervised), and ACP+AL+CA+CO (semi-supervised). The corresponding objective functions were: $\\mathcal {L}_{\\rm AL}$, $\\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$, $\\mathcal {L}_{\\rm ACP}$, and $\\mathcal {L}_{\\rm ACP} + \\mathcal {L}_{\\rm AL} + \\mathcal {L}_{\\rm CA} + \\mathcal {L}_{\\rm CO}$." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "2cfd959e433f290bb50b55722370f0d22fe090b7" ] }, { "annotation_id": [ "49a78a07d2eed545556a835ccf2eb40e5eee9801", "acd6d15bd67f4b1496ee8af1c93c33e7d59c89e1" ], "answer": [ { "evidence": [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event." ], "extractive_spans": [], "free_form_answer": "based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event ", "highlighted_evidence": [ "As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event." ], "unanswerable": false, "yes_no": null }, { "evidence": [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.", "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation. Based on the availability of scores and the types of discourse relations, we classify the extracted event pairs into the following three types." ], "extractive_spans": [], "free_form_answer": "cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity", "highlighted_evidence": [ "As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.", "The seed lexicon consists of positive and negative predicates. If the predicate of an extracted event is in the seed lexicon and does not involve complex phenomena like negation, we assign the corresponding polarity score ($+1$ for positive events and $-1$ for negative events) to the event. We expect the model to automatically learn complex phenomena through label propagation." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "2cfd959e433f290bb50b55722370f0d22fe090b7" ] }, { "annotation_id": [ "36926a4c9e14352c91111150aa4c6edcc5c0770f", "75b6dd28ccab20a70087635d89c2b22d0e99095c" ], "answer": [ { "evidence": [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.", "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.", "FLOAT SELECTED: Table 1: Statistics of the AL, CA, and CO datasets.", "We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well. Extracted from Japanese websites using HTML layouts and linguistic patterns, the dataset covered various genres. For example, the following two sentences were labeled positive and negative, respectively:", "Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19.", "FLOAT SELECTED: Table 2: Details of the ACP dataset." ], "extractive_spans": [], "free_form_answer": "7000000 pairs of events were extracted from the Japanese Web corpus, 529850 pairs of events were extracted from the ACP corpus", "highlighted_evidence": [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. ", "From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16.", "FLOAT SELECTED: Table 1: Statistics of the AL, CA, and CO datasets.", "We used the latest version of the ACP Corpus BIBREF12 for evaluation. It was used for (semi-)supervised training as well.", "Although the ACP corpus was originally constructed in the context of sentiment analysis, we found that it could roughly be regarded as a collection of affective events. We parsed each sentence and extracted the last clause in it. The train/dev/test split of the data is shown in Table TABREF19.", "FLOAT SELECTED: Table 2: Details of the ACP dataset." ], "unanswerable": false, "yes_no": null }, { "evidence": [ "FLOAT SELECTED: Table 2: Details of the ACP dataset." ], "extractive_spans": [], "free_form_answer": "The ACP corpus has around 700k events split into positive and negative polarity ", "highlighted_evidence": [ "FLOAT SELECTED: Table 2: Details of the ACP dataset." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "2cfd959e433f290bb50b55722370f0d22fe090b7", "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4" ] }, { "annotation_id": [ "2d8c7df145c37aad905e48f64d8caa69e54434d4" ], "answer": [ { "evidence": [ "Affective events BIBREF0 are events that typically affect people in positive or negative ways. For example, getting money and playing sports are usually positive to the experiencers; catching cold and losing one's wallet are negative. Understanding affective events is important to various natural language processing (NLP) applications such as dialogue systems BIBREF1, question-answering systems BIBREF2, and humor recognition BIBREF3. In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive)." ], "extractive_spans": [ "negative", "positive" ], "free_form_answer": "", "highlighted_evidence": [ "In this paper, we work on recognizing the polarity of an affective event that is represented by a score ranging from $-1$ (negative) to 1 (positive)." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58" ] }, { "annotation_id": [ "df4372b2e8d9bb2039a5582f192768953b01d904" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data." ], "extractive_spans": [], "free_form_answer": "3%", "highlighted_evidence": [ "FLOAT SELECTED: Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58" ] }, { "annotation_id": [ "5c5bbc8af91c16af89b4ddd57ee6834be018e4e7" ], "answer": [ { "evidence": [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event." ], "extractive_spans": [], "free_form_answer": "by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity", "highlighted_evidence": [ "In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive)." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58" ] }, { "annotation_id": [ "0206f2131f64a3e02498cedad1250971b78ffd0c" ], "answer": [ { "evidence": [ "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16." ], "extractive_spans": [], "free_form_answer": "30 words", "highlighted_evidence": [ "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58" ] }, { "annotation_id": [ "c36bad2758c4f9866d64c357c475d370595d937f" ], "answer": [ { "evidence": [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. To extract event pairs tagged with discourse relations, we used the Japanese dependency parser KNP and in-house postprocessing scripts BIBREF14. KNP used hand-written rules to segment each sentence into what we conventionally called clauses (mostly consecutive text chunks), each of which contained one main predicate. KNP also identified the discourse relations of event pairs if explicit discourse connectives BIBREF4 such as “ので” (because) and “のに” (in spite of) were present. We treated Cause/Reason (原因・理由) and Condition (条件) in the original tagset BIBREF15 as Cause and Concession (逆接) as Concession, respectively. Here is an example of event pair extraction.", "We constructed our seed lexicon consisting of 15 positive words and 15 negative words, as shown in Section SECREF27. From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO. We randomly selected subsets of AL event pairs such that positive and negative latter events were equal in size. We also sampled event pairs for each of CA and CO such that it was five times larger than AL. The results are shown in Table TABREF16." ], "extractive_spans": [ "100 million sentences" ], "free_form_answer": "", "highlighted_evidence": [ "As a raw corpus, we used a Japanese web corpus that was compiled through the procedures proposed by BIBREF13. ", "From the corpus of about 100 million sentences, we obtained 1.4 millions event pairs for AL, 41 millions for CA, and 6 millions for CO." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58" ] } ], "nlp_background": [ "two", "two", "two", "two", "zero", "zero", "zero", "zero", "zero" ], "paper_read": [ "no", "no", "no", "no", "no", "no", "no", "no", "no" ], "question": [ "What is the seed lexicon?", "What are the results?", "How are relations used to propagate polarity?", "How big is the Japanese data?", "What are labels available in dataset for supervision?", "How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?", "How does their model learn using mostly raw data?", "How big is seed lexicon used for training?", "How large is raw corpus used for training?" ], "question_id": [ "753990d0b621d390ed58f20c4d9e4f065f0dc672", "9d578ddccc27dd849244d632dd0f6bf27348ad81", "02e4bf719b1a504e385c35c6186742e720bcb281", "44c4bd6decc86f1091b5fc0728873d9324cdde4e", "86abeff85f3db79cf87a8c993e5e5aa61226dc98", "c029deb7f99756d2669abad0a349d917428e9c12", "39f8db10d949c6b477fa4b51e7c184016505884f", "d0bc782961567dc1dd7e074b621a6d6be44bb5b4", "a592498ba2fac994cd6fad7372836f0adb37e22a" ], "question_writer": [ "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "c1fbdd7a261021041f75fbe00a55b4c386ebbbb4", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c" ], "search_query": [ "", "", "", "", "", "", "", "", "" ], "topic_background": [ "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar" ] }

{ "caption": [ "Figure 1: An overview of our method. We focus on pairs of events, the former events and the latter events, which are connected with a discourse relation, CAUSE or CONCESSION. Dropped pronouns are indicated by brackets in English translations. We divide the event pairs into three types: AL, CA, and CO. In AL, the polarity of a latter event is automatically identified as either positive or negative, according to the seed lexicon (the positive word is colored red and the negative word blue). We propagate the latter event’s polarity to the former event. The same polarity as the latter event is used for the discourse relation CAUSE, and the reversed polarity for CONCESSION. In CA and CO, the latter event’s polarity is not known. Depending on the discourse relation, we encourage the two events’ polarities to be the same (CA) or reversed (CO). Details are given in Section 3.2.", "Table 1: Statistics of the AL, CA, and CO datasets.", "Table 2: Details of the ACP dataset.", "Table 5: Examples of polarity scores predicted by the BiGRU model trained with AL+CA+CO.", "Table 3: Performance of various models on the ACP test set.", "Table 4: Results for small labeled training data. Given the performance with the full dataset, we show BERT trained only with the AL data." ], "file": [ "2-Figure1-1.png", "4-Table1-1.png", "4-Table2-1.png", "5-Table5-1.png", "5-Table3-1.png", "5-Table4-1.png" ] }

[ "What is the seed lexicon?", "What are the results?", "How are relations used to propagate polarity?", "How big is the Japanese data?", "How big are improvements of supervszed learning results trained on smalled labeled data enhanced with proposed approach copared to basic approach?", "How does their model learn using mostly raw data?", "How big is seed lexicon used for training?" ]

[ [ "1909.00694-Proposed Method ::: Discourse Relation-Based Event Pairs-1" ], [ "1909.00694-Experiments ::: Model Configurations-2", "1909.00694-5-Table4-1.png", "1909.00694-Experiments ::: Model Configurations-0", "1909.00694-5-Table3-1.png" ], [ "1909.00694-Proposed Method ::: Discourse Relation-Based Event Pairs-1", "1909.00694-Introduction-2" ], [ "1909.00694-Experiments ::: Dataset ::: AL, CA, and CO-4", "1909.00694-4-Table1-1.png", "1909.00694-Experiments ::: Dataset ::: AL, CA, and CO-0", "1909.00694-Experiments ::: Dataset ::: ACP (ACP Corpus)-5", "1909.00694-Experiments ::: Dataset ::: ACP (ACP Corpus)-0", "1909.00694-4-Table2-1.png" ], [ "1909.00694-5-Table4-1.png" ], [ "1909.00694-Introduction-2" ], [ "1909.00694-Experiments ::: Dataset ::: AL, CA, and CO-4" ] ]

[ "a vocabulary of positive and negative predicates that helps determine the polarity score of an event", "Using all data to train: AL -- BiGRU achieved 0.843 accuracy, AL -- BERT achieved 0.863 accuracy, AL+CA+CO -- BiGRU achieved 0.866 accuracy, AL+CA+CO -- BERT achieved 0.835, accuracy, ACP -- BiGRU achieved 0.919 accuracy, ACP -- BERT achived 0.933, accuracy, ACP+AL+CA+CO -- BiGRU achieved 0.917 accuracy, ACP+AL+CA+CO -- BERT achieved 0.913 accuracy. \nUsing a subset to train: BERT achieved 0.876 accuracy using ACP (6K), BERT achieved 0.886 accuracy using ACP (6K) + AL, BiGRU achieved 0.830 accuracy using ACP (6K), BiGRU achieved 0.879 accuracy using ACP (6K) + AL + CA + CO.", "cause relation: both events in the relation should have the same polarity; concession relation: events should have opposite polarity", "The ACP corpus has around 700k events split into positive and negative polarity ", "3%", "by exploiting discourse relations to propagate polarity from seed predicates to final sentiment polarity", "30 words" ]

{ "paragraphs": [ [ "“If each city is like a game of chess, the day when I have learned the rules, I shall finally possess my empire, even if I shall never succeed in knowing all the cities it contains.”", "", "— Italo Calvino, Invisible Cities", "A community's identity—defined through the common interests and shared experiences of its users—shapes various facets of the social dynamics within it BIBREF0 , BIBREF1 , BIBREF2 . Numerous instances of this interplay between a community's identity and social dynamics have been extensively studied in the context of individual online communities BIBREF3 , BIBREF4 , BIBREF5 . However, the sheer variety of online platforms complicates the task of generalizing insights beyond these isolated, single-community glimpses. A new way to reason about the variation across multiple communities is needed in order to systematically characterize the relationship between properties of a community and the dynamics taking place within.", "One especially important component of community dynamics is user engagement. We can aim to understand why users join certain communities BIBREF6 , what factors influence user retention BIBREF7 , and how users react to innovation BIBREF5 . While striking patterns of user engagement have been uncovered in prior case studies of individual communities BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 , BIBREF12 , we do not know whether these observations hold beyond these cases, or when we can draw analogies between different communities. Are there certain types of communities where we can expect similar or contrasting engagement patterns?", "To address such questions quantitatively we need to provide structure to the diverse and complex space of online communities. Organizing the multi-community landscape would allow us to both characterize individual points within this space, and reason about systematic variations in patterns of user engagement across the space.", "Present work: Structuring the multi-community space. In order to systematically understand the relationship between community identityand user engagement we introduce a quantitative typology of online communities. Our typology is based on two key aspects of community identity: how distinctive—or niche—a community's interests are relative to other communities, and how dynamic—or volatile—these interests are over time. These axes aim to capture the salience of a community's identity and dynamics of its temporal evolution.", "Our main insight in implementing this typology automatically and at scale is that the language used within a community can simultaneously capture how distinctive and dynamic its interests are. This language-based approach draws on a wealth of literature characterizing linguistic variation in online communities and its relationship to community and user identity BIBREF16 , BIBREF5 , BIBREF17 , BIBREF18 , BIBREF19 . Basing our typology on language is also convenient since it renders our framework immediately applicable to a wide variety of online communities, where communication is primarily recorded in a textual format.", "Using our framework, we map almost 300 Reddit communities onto the landscape defined by the two axes of our typology (Section SECREF2 ). We find that this mapping induces conceptually sound categorizations that effectively capture key aspects of community-level social dynamics. In particular, we quantitatively validate the effectiveness of our mapping by showing that our two-dimensional typology encodes signals that are predictive of community-level rates of user retention, complementing strong activity-based features.", "Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.", "More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities.", "More generally, our methodology stands as an example of how sociological questions can be addressed in a multi-community setting. In performing our analyses across a rich variety of communities, we reveal both the diversity of phenomena that can occur, as well as the systematic nature of this diversity." ], [ "A community's identity derives from its members' common interests and shared experiences BIBREF15 , BIBREF20 . In this work, we structure the multi-community landscape along these two key dimensions of community identity: how distinctive a community's interests are, and how dynamic the community is over time.", "We now proceed to outline our quantitative typology, which maps communities along these two dimensions. We start by providing an intuition through inspecting a few example communities. We then introduce a generalizable language-based methodology and use it to map a large set of Reddit communities onto the landscape defined by our typology of community identity." ], [ "In order to illustrate the diversity within the multi-community space, and to provide an intuition for the underlying structure captured by the proposed typology, we first examine a few example communities and draw attention to some key social dynamics that occur within them.", "We consider four communities from Reddit: in Seahawks, fans of the Seahawks football team gather to discuss games and players; in BabyBumps, expecting mothers trade advice and updates on their pregnancy; Cooking consists of recipe ideas and general discussion about cooking; while in pics, users share various images of random things (like eels and hornets). We note that these communities are topically contrasting and foster fairly disjoint user bases. Additionally, these communities exhibit varied patterns of user engagement. While Seahawks maintains a devoted set of users from month to month, pics is dominated by transient users who post a few times and then depart.", "Discussions within these communities also span varied sets of interests. Some of these interests are more specific to the community than others: risotto, for example, is seldom a discussion point beyond Cooking. Additionally, some interests consistently recur, while others are specific to a particular time: kitchens are a consistent focus point for cooking, but mint is only in season during spring. Coupling specificity and consistency we find interests such as easter, which isn't particularly specific to BabyBumps but gains prominence in that community around Easter (see Figure FIGREF3 .A for further examples).", "These specific interests provide a window into the nature of the communities' interests as a whole, and by extension their community identities. Overall, discussions in Cooking focus on topics which are highly distinctive and consistently recur (like risotto). In contrast, discussions in Seahawks are highly dynamic, rapidly shifting over time as new games occur and players are traded in and out. In the remainder of this section we formally introduce a methodology for mapping communities in this space defined by their distinctiveness and dynamicity (examples in Figure FIGREF3 .B)." ], [ "Our approach follows the intuition that a distinctive community will use language that is particularly specific, or unique, to that community. Similarly, a dynamic community will use volatile language that rapidly changes across successive windows of time. To capture this intuition automatically, we start by defining word-level measures of specificity and volatility. We then extend these word-level primitives to characterize entire comments, and the community itself.", "Our characterizations of words in a community are motivated by methodology from prior literature that compares the frequency of a word in a particular setting to its frequency in some background distribution, in order to identify instances of linguistic variation BIBREF21 , BIBREF19 . Our particular framework makes this comparison by way of pointwise mutual information (PMI).", "In the following, we use INLINEFORM0 to denote one community within a set INLINEFORM1 of communities, and INLINEFORM2 to denote one time period within the entire history INLINEFORM3 of INLINEFORM4 . We account for temporal as well as inter-community variation by computing word-level measures for each time period of each community's history, INLINEFORM5 . Given a word INLINEFORM6 used within a particular community INLINEFORM7 at time INLINEFORM8 , we define two word-level measures:", "Specificity. We quantify the specificity INLINEFORM0 of INLINEFORM1 to INLINEFORM2 by calculating the PMI of INLINEFORM3 and INLINEFORM4 , relative to INLINEFORM5 , INLINEFORM6 ", "where INLINEFORM0 is INLINEFORM1 's frequency in INLINEFORM2 . INLINEFORM3 is specific to INLINEFORM4 if it occurs more frequently in INLINEFORM5 than in the entire set INLINEFORM6 , hence distinguishing this community from the rest. A word INLINEFORM7 whose occurrence is decoupled from INLINEFORM8 , and thus has INLINEFORM9 close to 0, is said to be generic.", "We compute values of INLINEFORM0 for each time period INLINEFORM1 in INLINEFORM2 ; in the above description we drop the time-based subscripts for clarity.", "Volatility. We quantify the volatility INLINEFORM0 of INLINEFORM1 to INLINEFORM2 as the PMI of INLINEFORM3 and INLINEFORM4 relative to INLINEFORM5 , the entire history of INLINEFORM6 : INLINEFORM7 ", "A word INLINEFORM0 is volatile at time INLINEFORM1 in INLINEFORM2 if it occurs more frequently at INLINEFORM3 than in the entire history INLINEFORM4 , behaving as a fad within a small window of time. A word that occurs with similar frequency across time, and hence has INLINEFORM5 close to 0, is said to be stable.", "Extending to utterances. Using our word-level primitives, we define the specificity of an utterance INLINEFORM0 in INLINEFORM1 , INLINEFORM2 as the average specificity of each word in the utterance. The volatility of utterances is defined analogously.", "" ], [ "Having described these word-level measures, we now proceed to establish the primary axes of our typology:", "Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less distinctive identity as being generic.", "Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable.", "In our subsequent analyses, we focus mostly on examing the average distinctiveness and dynamicity of a community over time, denoted INLINEFORM0 and INLINEFORM1 ." ], [ "We now explain how our typology can be applied to the particular setting of Reddit, and describe the overall behaviour of our linguistic axes in this context.", "Dataset description. Reddit is a popular website where users form and participate in discussion-based communities called subreddits. Within these communities, users post content—such as images, URLs, or questions—which often spark vibrant lengthy discussions in thread-based comment sections.", "The website contains many highly active subreddits with thousands of active subscribers. These communities span an extremely rich variety of topical interests, as represented by the examples described earlier. They also vary along a rich multitude of structural dimensions, such as the number of users, the amount of conversation and social interaction, and the social norms determining which types of content become popular. The diversity and scope of Reddit's multicommunity ecosystem make it an ideal landscape in which to closely examine the relation between varying community identities and social dynamics.", "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 ).", "Estimating linguistic measures. We estimate word frequencies INLINEFORM0 , and by extension each downstream measure, in a carefully controlled manner in order to ensure we capture robust and meaningful linguistic behaviour. First, we only consider top-level comments which are initial responses to a post, as the content of lower-level responses might reflect conventions of dialogue more than a community's high-level interests. Next, in order to prevent a few highly active users from dominating our frequency estimates, we count each unique word once per user, ignoring successive uses of the same word by the same user. This ensures that our word-level characterizations are not skewed by a small subset of highly active contributors.", "In our subsequent analyses, we will only look at these measures computed over the nouns used in comments. In principle, our framework can be applied to any choice of vocabulary. However, in the case of Reddit using nouns provides a convenient degree of interpretability. We can easily understand the implication of a community preferentially mentioning a noun such as gamer or feminist, but interpreting the overuse of verbs or function words such as take or of is less straightforward. Additionally, in focusing on nouns we adopt the view emphasized in modern “third wave” accounts of sociolinguistic variation, that stylistic variation is inseparable from topical content BIBREF23 . In the case of online communities, the choice of what people choose to talk about serves as a primary signal of social identity. That said, a typology based on more purely stylistic differences is an interesting avenue for future work.", "Accounting for rare words. One complication when using measures such as PMI, which are based off of ratios of frequencies, is that estimates for very infrequent words could be overemphasized BIBREF24 . Words that only appear a few times in a community tend to score at the extreme ends of our measures (e.g. as highly specific or highly generic), obfuscating the impact of more frequent words in the community. To address this issue, we discard the long tail of infrequent words in our analyses, using only the top 5th percentile of words, by frequency within each INLINEFORM0 , to score comments and communities.", "Typology output on Reddit. The distribution of INLINEFORM0 and INLINEFORM1 across Reddit communities is shown in Figure FIGREF3 .B, along with examples of communities at the extremes of our typology. We find that interpretable groupings of communities emerge at various points within our axes. For instance, highly distinctive and dynamic communities tend to focus on rapidly-updating interests like sports teams and games, while generic and consistent communities tend to be large “link-sharing” hubs where users generally post content with no clear dominating themes. More examples of communities at the extremes of our typology are shown in Table TABREF9 .", "We note that these groupings capture abstract properties of a community's content that go beyond its topic. For instance, our typology relates topically contrasting communities such as yugioh (which is about a popular trading card game) and Seahawks through the shared trait that their content is particularly distinctive. Additionally, the axes can clarify differences between topically similar communities: while startrek and thewalkingdead both focus on TV shows, startrek is less dynamic than the median community, while thewalkingdead is among the most dynamic communities, as the show was still airing during the years considered." ], [ "We have seen that our typology produces qualitatively satisfying groupings of communities according to the nature of their collective identity. This section shows that there is an informative and highly predictive relationship between a community's position in this typology and its user engagement patterns. We find that communities with distinctive and dynamic identities have higher rates of user engagement, and further show that a community's position in our identity-based landscape holds important predictive information that is complementary to a strong activity baseline.", "In particular user retention is one of the most crucial aspects of engagement and is critical to community maintenance BIBREF2 . We quantify how successful communities are at retaining users in terms of both short and long-term commitment. Our results indicate that rates of user retention vary drastically, yet systematically according to how distinctive and dynamic a community is (Figure FIGREF3 ).", "We find a strong, explanatory relationship between the temporal consistency of a community's identity and rates of user engagement: dynamic communities that continually update and renew their discussion content tend to have far higher rates of user engagement. The relationship between distinctiveness and engagement is less universal, but still highly informative: niche communities tend to engender strong, focused interest from users at one particular point in time, though this does not necessarily translate into long-term retention." ], [ "We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).", "Monthly retention is formally defined as the proportion of users who contribute in month INLINEFORM0 and then return to contribute again in month INLINEFORM1 . Each monthly datapoint is treated as unique and the trends in Figure FIGREF11 show 95% bootstrapped confidence intervals, cluster-resampled at the level of subreddit BIBREF25 , to account for differences in the number of months each subreddit contributes to the data.", "Importantly, we find that in the task of predicting community-level user retention our identity-based typology holds additional predictive value on top of strong baseline features based on community-size (# contributing users) and activity levels (mean # contributions per user), which are commonly used for churn prediction BIBREF7 . We compared out-of-sample predictive performance via leave-one-community-out cross validation using random forest regressors with ensembles of size 100, and otherwise default hyperparameters BIBREF26 . A model predicting average monthly retention based on a community's average distinctiveness and dynamicity achieves an average mean squared error ( INLINEFORM0 ) of INLINEFORM1 and INLINEFORM2 , while an analogous model predicting based on a community's size and average activity level (both log-transformed) achieves INLINEFORM4 and INLINEFORM5 . The difference between the two models is not statistically significant ( INLINEFORM6 , Wilcoxon signed-rank test). However, combining features from both models results in a large and statistically significant improvement over each independent model ( INLINEFORM7 , INLINEFORM8 , INLINEFORM9 Bonferroni-corrected pairwise Wilcoxon tests). These results indicate that our typology can explain variance in community-level retention rates, and provides information beyond what is present in standard activity-based features." ], [ "As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content.", "To measure user tenures we focused on one slice of data (May, 2013) and measured how many months a user spends in each community, on average—the average number of months between a user's first and last comment in each community. We have activity data up until May 2015, so the maximum tenure is 24 months in this set-up, which is exceptionally long relative to the average community member (throughout our entire data less than INLINEFORM0 of users have tenures of more than 24 months in any community)." ], [ "The previous section shows that there is a strong connection between the nature of a community's identity and its basic user engagement patterns. In this section, we probe the relationship between a community's identity and how permeable, or accessible, it is to outsiders.", "We measure this phenomenon using what we call the acculturation gap, which compares the extent to which engaged vs. non-engaged users employ community-specific language. While previous work has found this gap to be large and predictive of future user engagement in two beer-review communities BIBREF5 , we find that the size of the acculturation gap depends strongly on the nature of a community's identity, with the gap being most pronounced in stable, highly distinctive communities (Figure FIGREF13 ).", "This finding has important implications for our understanding of online communities. Though many works have analyzed the dynamics of “linguistic belonging” in online communities BIBREF16 , BIBREF28 , BIBREF5 , BIBREF17 , our results suggest that the process of linguistically fitting in is highly contingent on the nature of a community's identity. At one extreme, in generic communities like pics or worldnews there is no distinctive, linguistic identity for users to adopt.", "To measure the acculturation gap for a community, we follow Danescu-Niculescu-Mizil et al danescu-niculescu-mizilno2013 and build “snapshot language models” (SLMs) for each community, which capture the linguistic state of a community at one point of time. Using these language models we can capture how linguistically close a particular utterance is to the community by measuring the cross-entropy of this utterance relative to the SLM: DISPLAYFORM0 ", "where INLINEFORM0 is the probability assigned to bigram INLINEFORM1 from comment INLINEFORM2 in community-month INLINEFORM3 . We build the SLMs by randomly sampling 200 active users—defined as users with at least 5 comments in the respective community and month. For each of these 200 active users we select 5 random 10-word spans from 5 unique comments. To ensure robustness and maximize data efficiency, we construct 100 SLMs for each community-month pair that has enough data, bootstrap-resampling from the set of active users.", "We compute a basic measure of the acculturation gap for a community-month INLINEFORM0 as the relative difference of the cross-entropy of comments by users active in INLINEFORM1 with that of singleton comments by outsiders—i.e., users who only ever commented once in INLINEFORM2 , but who are still active in Reddit in general: DISPLAYFORM0 ", " INLINEFORM0 denotes the distribution over singleton comments, INLINEFORM1 denotes the distribution over comments from users active in INLINEFORM2 , and INLINEFORM3 the expected values of the cross-entropy over these respective distributions. For each bootstrap-sampled SLM we compute the cross-entropy of 50 comments by active users (10 comments from 5 randomly sampled active users, who were not used to construct the SLM) and 50 comments from randomly-sampled outsiders.", "Figure FIGREF13 .A shows that the acculturation gap varies substantially with how distinctive and dynamic a community is. Highly distinctive communities have far higher acculturation gaps, while dynamicity exhibits a non-linear relationship: relatively stable communities have a higher linguistic `entry barrier', as do very dynamic ones. Thus, in communities like IAmA (a general Q&A forum) that are very generic, with content that is highly, but not extremely dynamic, outsiders are at no disadvantage in matching the community's language. In contrast, the acculturation gap is large in stable, distinctive communities like Cooking that have consistent community-specific language. The gap is also large in extremely dynamic communities like Seahawks, which perhaps require more attention or interest on the part of active users to keep up-to-date with recent trends in content.", "These results show that phenomena like the acculturation gap, which were previously observed in individual communities BIBREF28 , BIBREF5 , cannot be easily generalized to a larger, heterogeneous set of communities. At the same time, we see that structuring the space of possible communities enables us to observe systematic patterns in how such phenomena vary." ], [ "Through the acculturation gap, we have shown that communities exhibit large yet systematic variations in their permeability to outsiders. We now turn to understanding the divide in commenting behaviour between outsiders and active community members at a finer granularity, by focusing on two particular ways in which such gaps might manifest among users: through different levels of engagement with specific content and with temporally volatile content.", "Echoing previous results, we find that community type mediates the extent and nature of the divide in content affinity. While in distinctive communities active members have a higher affinity for both community-specific content and for highly volatile content, the opposite is true for generic communities, where it is the outsiders who engage more with volatile content.", "We quantify these divides in content affinity by measuring differences in the language of the comments written by active users and outsiders. Concretely, for each community INLINEFORM0 , we define the specificity gap INLINEFORM1 as the relative difference between the average specificity of comments authored by active members, and by outsiders, where these measures are macroaveraged over users. Large, positive INLINEFORM2 then occur in communities where active users tend to engage with substantially more community-specific content than outsiders.", "We analogously define the volatility gap INLINEFORM0 as the relative difference in volatilities of active member and outsider comments. Large, positive values of INLINEFORM1 characterize communities where active users tend to have more volatile interests than outsiders, while negative values indicate communities where active users tend to have more stable interests.", "We find that in 94% of communities, INLINEFORM0 , indicating (somewhat unsurprisingly) that in almost all communities, active users tend to engage with more community-specific content than outsiders. However, the magnitude of this divide can vary greatly: for instance, in Homebrewing, which is dedicated to brewing beer, the divide is very pronounced ( INLINEFORM1 0.33) compared to funny, a large hub where users share humorous content ( INLINEFORM2 0.011).", "The nature of the volatility gap is comparatively more varied. In Homebrewing ( INLINEFORM0 0.16), as in 68% of communities, active users tend to write more volatile comments than outsiders ( INLINEFORM1 0). However, communities like funny ( INLINEFORM2 -0.16), where active users contribute relatively stable comments compared to outsiders ( INLINEFORM3 0), are also well-represented on Reddit.", "To understand whether these variations manifest systematically across communities, we examine the relationship between divides in content affinity and community type. In particular, following the intuition that active users have a relatively high affinity for a community's niche, we expect that the distinctiveness of a community will be a salient mediator of specificity and volatility gaps. Indeed, we find a strong correlation between a community's distinctiveness and its specificity gap (Spearman's INLINEFORM0 0.34, INLINEFORM1 0.001).", "We also find a strong correlation between distinctiveness and community volatility gaps (Spearman's INLINEFORM0 0.53, INLINEFORM1 0.001). In particular, we see that among the most distinctive communities (i.e., the top third of communities by distinctiveness), active users tend to write more volatile comments than outsiders (mean INLINEFORM2 0.098), while across the most generic communities (i.e., the bottom third), active users tend to write more stable comments (mean INLINEFORM3 -0.047, Mann-Whitney U test INLINEFORM4 0.001). The relative affinity of outsiders for volatile content in these communities indicates that temporally ephemeral content might serve as an entry point into such a community, without necessarily engaging users in the long term." ], [ "Our language-based typology and analysis of user engagement draws on and contributes to several distinct research threads, in addition to the many foundational studies cited in the previous sections.", "Multicommunity studies. Our investigation of user engagement in multicommunity settings follows prior literature which has examined differences in user and community dynamics across various online groups, such as email listservs. Such studies have primarily related variations in user behaviour to structural features such as group size and volume of content BIBREF30 , BIBREF31 , BIBREF32 , BIBREF33 . In focusing on the linguistic content of communities, we extend this research by providing a content-based framework through which user engagement can be examined.", "Reddit has been a particularly useful setting for studying multiple communities in prior work. Such studies have mostly focused on characterizing how individual users engage across a multi-community platform BIBREF34 , BIBREF35 , or on specific user engagement patterns such as loyalty to particular communities BIBREF22 . We complement these studies by seeking to understand how features of communities can mediate a broad array of user engagement patterns within them.", "Typologies of online communities. Prior attempts to typologize online communities have primarily been qualitative and based on hand-designed categories, making them difficult to apply at scale. These typologies often hinge on having some well-defined function the community serves, such as supporting a business or non-profit cause BIBREF36 , which can be difficult or impossible to identify in massive, anonymous multi-community settings. Other typologies emphasize differences in communication platforms and other functional requirements BIBREF37 , BIBREF38 , which are important but preclude analyzing differences between communities within the same multi-community platform. Similarly, previous computational methods of characterizing multiple communities have relied on the presence of markers such as affixes in community names BIBREF35 , or platform-specific affordances such as evaluation mechanisms BIBREF39 .", "Our typology is also distinguished from community detection techniques that rely on structural or functional categorizations BIBREF40 , BIBREF41 . While the focus of those studies is to identify and characterize sub-communities within a larger social network, our typology provides a characterization of pre-defined communities based on the nature of their identity.", "Broader work on collective identity. Our focus on community identity dovetails with a long line of research on collective identity and user engagement, in both online and offline communities BIBREF42 , BIBREF1 , BIBREF2 . These studies focus on individual-level psychological manifestations of collective (or social) identity, and their relationship to user engagement BIBREF42 , BIBREF43 , BIBREF44 , BIBREF0 .", "In contrast, we seek to characterize community identities at an aggregate level and in an interpretable manner, with the goal of systematically organizing the diverse space of online communities. Typologies of this kind are critical to these broader, social-psychological studies of collective identity: they allow researchers to systematically analyze how the psychological manifestations and implications of collective identity vary across diverse sets of communities." ], [ "Our current understanding of engagement patterns in online communities is patched up from glimpses offered by several disparate studies focusing on a few individual communities. This work calls into attention the need for a method to systematically reason about similarities and differences across communities. By proposing a way to structure the multi-community space, we find not only that radically contrasting engagement patterns emerge in different parts of this space, but also that this variation can be at least partly explained by the type of identity each community fosters.", "Our choice in this work is to structure the multi-community space according to a typology based on community identity, as reflected in language use. We show that this effectively explains cross-community variation of three different user engagement measures—retention, acculturation and content affinity—and complements measures based on activity and size with additional interpretable information. For example, we find that in niche communities established members are more likely to engage with volatile content than outsiders, while the opposite is true in generic communities. Such insights can be useful for community maintainers seeking to understand engagement patterns in their own communities.", "One main area of future research is to examine the temporal dynamics in the multi-community landscape. By averaging our measures of distinctiveness and dynamicity across time, our present study treated community identity as a static property. However, as communities experience internal changes and respond to external events, we can expect the nature of their identity to shift as well. For instance, the relative consistency of harrypotter may be disrupted by the release of a new novel, while Seahawks may foster different identities during and between football seasons. Conversely, a community's type may also mediate the impact of new events. Moving beyond a static view of community identity could enable us to better understand how temporal phenomena such as linguistic change manifest across different communities, and also provide a more nuanced view of user engagement—for instance, are communities more welcoming to newcomers at certain points in their lifecycle?", "Another important avenue of future work is to explore other ways of mapping the landscape of online communities. For example, combining structural properties of communities BIBREF40 with topical information BIBREF35 and with our identity-based measures could further characterize and explain variations in user engagement patterns. Furthermore, extending the present analyses to even more diverse communities supported by different platforms (e.g., GitHub, StackExchange, Wikipedia) could enable the characterization of more complex behavioral patterns such as collaboration and altruism, which become salient in different multicommunity landscapes." ], [ "The authors thank Liye Fu, Jack Hessel, David Jurgens and Lillian Lee for their helpful comments. This research has been supported in part by a Discovery and Innovation Research Seed Award from the Office of the Vice Provost for Research at Cornell, NSF CNS-1010921, IIS-1149837, IIS-1514268 NIH BD2K, ARO MURI, DARPA XDATA, DARPA SIMPLEX, DARPA NGS2, Stanford Data Science Initiative, SAP Stanford Graduate Fellowship, NSERC PGS-D, Boeing, Lightspeed, and Volkswagen. " ] ], "section_name": [ "Introduction", "A typology of community identity", "Overview and intuition", "Language-based formalization", "Community-level measures", "Applying the typology to Reddit", "Community identity and user retention", "Community-type and monthly retention", "Community-type and user tenure", "Community identity and acculturation", "Community identity and content affinity", "Further related work", "Conclusion and future work", "Acknowledgements" ] }

{ "answers": [ { "annotation_id": [ "04ae0cc420f69540ca11707ab8ecc07a89f803f7", "31d8f8ed7ba40b27c480f7caf7cfb48fba47bb07" ], "answer": [ { "evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [ "We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. " ], "unanswerable": false, "yes_no": false }, { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58", "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] }, { "annotation_id": [ "8a080f37fbbb5c6700422a346b944ef535fa725b" ], "answer": [ { "evidence": [ "We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).", "As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content." ], "extractive_spans": [], "free_form_answer": "Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.\n", "highlighted_evidence": [ "We find that dynamic communities, such as Seahawks or starcraft, have substantially higher rates of monthly user retention than more stable communities (Spearman's INLINEFORM0 = 0.70, INLINEFORM1 0.001, computed with community points averaged over months; Figure FIGREF11 .A, left). Similarly, more distinctive communities, like Cooking and Naruto, exhibit moderately higher monthly retention rates than more generic communities (Spearman's INLINEFORM2 = 0.33, INLINEFORM3 0.001; Figure FIGREF11 .A, right).", "As with monthly retention, we find a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community (Spearman's INLINEFORM0 = 0.41, INLINEFORM1 0.001, computed over all community points; Figure FIGREF11 .B, left). This verifies that the short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content. Interestingly, there is no significant relationship between distinctiveness and long-term engagement (Spearman's INLINEFORM2 = 0.03, INLINEFORM3 0.77; Figure FIGREF11 .B, right). Thus, while highly distinctive communities like RandomActsOfMakeup may generate focused commitment from users over a short period of time, such communities are unlikely to retain long-term users unless they also have sufficiently dynamic content." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] }, { "annotation_id": [ "f64ff06cfd16f9bd339512a6e85f0a7bc8b670f4" ], "answer": [ { "evidence": [ "Engagement and community identity. We apply our framework to understand how two important aspects of user engagement in a community—the community's propensity to retain its users (Section SECREF3 ), and its permeability to new members (Section SECREF4 )—vary according to the type of collective identity it fosters. We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.", "More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). Interestingly, while established members of distinctive communities more avidly respond to temporal updates than newcomers, in more generic communities it is the outsiders who engage more with volatile content, perhaps suggesting that such content may serve as an entry-point to the community (but not necessarily a reason to stay). Such insights into the relation between collective identity and user engagement can be informative to community maintainers seeking to better understand growth patterns within their online communities." ], "extractive_spans": [ "communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members", "within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers " ], "free_form_answer": "", "highlighted_evidence": [ "We find that communities that are characterized by specialized, constantly-updating content have higher user retention rates, but also exhibit larger linguistic gaps that separate newcomers from established members.", "More closely examining factors that could contribute to this linguistic gap, we find that especially within distinctive communities, established users have an increased propensity to engage with the community's specialized content, compared to newcomers (Section SECREF5 ). " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] }, { "annotation_id": [ "2c804f9b9543e3b085fbd1fff87f0fde688f1484", "78de92427e9e37b0dfdc19f57b735e65cec40e0a" ], "answer": [ { "evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], "extractive_spans": [], "free_form_answer": "They selected all the subreddits from January 2013 to December 2014 with at least 500 words in the vocabulary and at least 4 months of the subreddit's history. They also removed communities with the bulk of the contributions are in foreign language.", "highlighted_evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. " ], "unanswerable": false, "yes_no": null }, { "evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], "extractive_spans": [], "free_form_answer": "They collect subreddits from January 2013 to December 2014,2 for which there are at\nleast 500 words in the vocabulary used to estimate the measures,\nin at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language.", "highlighted_evidence": [ "Our full dataset consists of all subreddits on Reddit from January 2013 to December 2014, for which there are at least 500 words in the vocabulary used to estimate our measures, in at least 4 months of the subreddit's history. We compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language. This results in 283 communities ( INLINEFORM0 ), for a total of 4,872 community-months ( INLINEFORM1 )." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "c1018a31c3272ce74964a3280069f62f314a1a58", "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] }, { "annotation_id": [ "62d30e963bf86e9b2d454adbd4b2c4dc3107cd11" ], "answer": [ { "evidence": [ "Dynamicity. A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . We refer to a community whose language is relatively consistent throughout time as being stable." ], "extractive_spans": [ "the average volatility of all utterances" ], "free_form_answer": "", "highlighted_evidence": [ ". A highly dynamic community constantly shifts interests from one time window to another, and these temporal variations are reflected in its use of volatile language. Formally, we define the dynamicity of a community INLINEFORM0 as the average volatility of all utterances in INLINEFORM1 . " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] }, { "annotation_id": [ "21484dfac315192bb69aee597ebf5d100ff5925b" ], "answer": [ { "evidence": [ "Distinctiveness. A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 . We refer to a community with a less distinctive identity as being generic." ], "extractive_spans": [ " the average specificity of all utterances" ], "free_form_answer": "", "highlighted_evidence": [ "A community with a very distinctive identity will tend to have distinctive interests, expressed through specialized language. Formally, we define the distinctiveness of a community INLINEFORM0 as the average specificity of all utterances in INLINEFORM1 " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "34c35a1877e453ecaebcf625df3ef788e1953cc4" ] } ], "nlp_background": [ "five", "five", "five", "five", "five", "five" ], "paper_read": [ "", "", "", "", "", "" ], "question": [ "Do they report results only on English data?", "How do the various social phenomena examined manifest in different types of communities?", "What patterns do they observe about how user engagement varies with the characteristics of a community?", "How did the select the 300 Reddit communities for comparison?", "How do the authors measure how temporally dynamic a community is?", "How do the authors measure how distinctive a community is?" ], "question_id": [ "003f884d3893532f8c302431c9f70be6f64d9be8", "bb97537a0a7c8f12a3f65eba73cefa6abcd2f2b2", "eea089baedc0ce80731c8fdcb064b82f584f483a", "edb2d24d6d10af13931b3a47a6543bd469752f0c", "938cf30c4f1d14fa182e82919e16072fdbcf2a82", "93f4ad6568207c9bd10d712a52f8de25b3ebadd4" ], "question_writer": [ "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37", "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37", "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37", "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37", "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37", "e8b24c3133e0bec0a6465e1f13acd3a5ed816b37" ], "search_query": [ "", "", "", "", "", "" ], "topic_background": [ "", "", "", "", "", "" ] }

{ "caption": [ "Figure 1: A: Within a community certain words are more community-specific and temporally volatile than others. For instance, words like onesies are highly specific to the BabyBumps community (top left corner), while words like easter are temporally ephemeral. B: Extending these word-level measures to communities, we can measure the overall distinctiveness and dynamicity of a community, which are highly associated with user retention rates (colored heatmap; see Section 3). Communities like Seahawks (a football team) and Cooking use highly distinctive language. Moreover, Seahawks uses very dynamic language, as the discussion continually shifts throughout the football season. In contrast, the content of Cooking remains stable over time, as does the content of pics; though these communities do have ephemeral fads, the overall themes discussed generally remain stable.", "Table 1: Examples of communities on Reddit which occur at the extremes (top and bottom quartiles) of our typology.", "Figure 2: A: The monthly retention rate for communities differs drastically according to their position in our identity-based typology, with dynamicity being the strongest signal of higher user retention (x-axes bin community-months by percentiles; in all subsequent plots, error bars indicate 95% bootstrapped confidence intervals). B: Dynamicity also correlates with long-term user retention, measured as the number of months the average user spends in the community; however, distinctiveness does not correlate with this longer-term variant of user retention.", "Figure 3: A: There is substantial variation in the direction and magnitude of the acculturation gap, which quantifies the extent to which established members of a community are linguistically differentiated from outsiders. Among 60% of communities this gap is positive, indicating that established users match the community’s language more than outsiders. B: The size of the acculturation gap varies systematically according to how dynamic and distinctive a community is. Distinctive communities exhibit larger gaps; as do relatively stable, and very dynamic communities." ], "file": [ "3-Figure1-1.png", "4-Table1-1.png", "5-Figure2-1.png", "6-Figure3-1.png" ] }

[ "How do the various social phenomena examined manifest in different types of communities?", "How did the select the 300 Reddit communities for comparison?" ]

[ [ "1705.09665-Community-type and user tenure-0", "1705.09665-Community-type and monthly retention-0" ], [ "1705.09665-Applying the typology to Reddit-3" ] ]

[ "Dynamic communities have substantially higher rates of monthly user retention than more stable communities. More distinctive communities exhibit moderately higher monthly retention rates than more generic communities. There is also a strong positive relationship between a community's dynamicity and the average number of months that a user will stay in that community - a short-term trend observed for monthly retention translates into longer-term engagement and suggests that long-term user retention might be strongly driven by the extent to which a community continually provides novel content.\n", "They collect subreddits from January 2013 to December 2014,2 for which there are at\nleast 500 words in the vocabulary used to estimate the measures,\nin at least 4 months of the subreddit’s history. They compute our measures over the comments written by users in a community in time windows of months, for each sufficiently active month, and manually remove communities where the bulk of the contributions are in a foreign language." ]

{ "paragraphs": [ [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.", "Traditionally, CTS tasks can be addressed by rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2, task-specific end-to-end methods BIBREF3, BIBREF4, BIBREF5, BIBREF6 and pipeline methods BIBREF7, BIBREF8, BIBREF9. Rule and dictionary based methods suffer from costly human-designed extraction rules, while task-specific end-to-end methods have non-uniform output formats and require task-specific training dataset. Pipeline methods break down the entire process into several pieces which improves the performance and generality. However, when the pipeline depth grows, error propagation will have a greater impact on the performance.", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.", "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.", "Experimental results show that QA-CTS task leads to significant improvement due to shared dataset. Our proposed model also achieves significantly better performance than the strong baseline methods. In addition, we also show that two-stage training mechanism has a great improvement on QA-CTS task.", "The rest of the paper is organized as follows. We briefly review the related work on clinical text structuring in Section SECREF2. Then, we present question answer based clinical text structuring task in Section SECREF3. In Section SECREF4, we present an effective model for this task. Section SECREF5 is devoted to computational studies and several investigations on the key issues of our proposed model. Finally, conclusions are given in Section SECREF6." ], [ "Clinical text structuring is a final problem which is highly related to practical applications. Most of existing studies are case-by-case. Few of them are developed for the general purpose structuring task. These studies can be roughly divided into three categories: rule and dictionary based methods, task-specific end-to-end methods and pipeline methods.", "Rule and dictionary based methods BIBREF0, BIBREF1, BIBREF2 rely extremely on heuristics and handcrafted extraction rules which is more of an art than a science and incurring extensive trial-and-error experiments. Fukuda et al. BIBREF0 identified protein names from biological papers by dictionaries and several features of protein names. Wang et al. BIBREF1 developed some linguistic rules (i.e. normalised/expanded term matching and substring term matching) to map specific terminology to SNOMED CT. Song et al. BIBREF2 proposed a hybrid dictionary-based bio-entity extraction technique and expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. This kind of approach features its interpretability and easy modifiability. However, with the increase of the rule amount, supplementing new rules to existing system will turn to be a rule disaster.", "Task-specific end-to-end methods BIBREF3, BIBREF4 use large amount of data to automatically model the specific task. Topaz et al. BIBREF3 constructed an automated wound information identification model with five output. Tan et al. BIBREF4 identified patients undergoing radical cystectomy for bladder cancer. Although they achieved good performance, none of their models could be used to another task due to output format difference. This makes building a new model for a new task a costly job.", "Pipeline methods BIBREF7, BIBREF8, BIBREF9 break down the entire task into several basic natural language processing tasks. Bill et al. BIBREF7 focused on attributes extraction which mainly relied on dependency parsing and named entity recognition BIBREF10, BIBREF11, BIBREF12. Meanwhile, Fonferko et al. BIBREF9 used more components like noun phrase chunking BIBREF13, BIBREF14, BIBREF15, part-of-speech tagging BIBREF16, BIBREF17, BIBREF18, sentence splitter, named entity linking BIBREF19, BIBREF20, BIBREF21, relation extraction BIBREF22, BIBREF23. This kind of method focus on language itself, so it can handle tasks more general. However, as the depth of pipeline grows, it is obvious that error propagation will be more and more serious. In contrary, using less components to decrease the pipeline depth will lead to a poor performance. So the upper limit of this method depends mainly on the worst component." ], [ "Recently, some works focused on pre-trained language representation models to capture language information from text and then utilizing the information to improve the performance of specific natural language processing tasks BIBREF24, BIBREF25, BIBREF26, BIBREF27 which makes language model a shared model to all natural language processing tasks. Radford et al. BIBREF24 proposed a framework for fine-tuning pre-trained language model. Peters et al. BIBREF25 proposed ELMo which concatenates forward and backward language models in a shallow manner. Devlin et al. BIBREF26 used bidirectional Transformers to model deep interactions between the two directions. Yang et al. BIBREF27 replaced the fixed forward or backward factorization order with all possible permutations of the factorization order and avoided using the [MASK] tag which causes pretrain-finetune discrepancy that BERT is subject to.", "The main motivation of introducing pre-trained language model is to solve the shortage of labeled data and polysemy problem. Although polysemy problem is not a common phenomenon in biomedical domain, shortage of labeled data is always a non-trivial problem. Lee et al. BIBREF28 applied BERT on large-scale biomedical unannotated data and achieved improvement on biomedical named entity recognition, relation extraction and question answering. Kim et al. BIBREF29 adapted BioBERT into multi-type named entity recognition and discovered new entities. Both of them demonstrates the usefulness of introducing pre-trained language model into biomedical domain." ], [ "Given a sequence of paragraph text $X=<x_1, x_2, ..., x_n>$, clinical text structuring (CTS) can be regarded to extract or generate a key-value pair where key $Q$ is typically a query term such as proximal resection margin and value $V$ is a result of query term $Q$ according to the paragraph text $X$.", "Generally, researchers solve CTS problem in two steps. Firstly, the answer-related text is pick out. And then several steps such as entity names conversion and negative words recognition are deployed to generate the final answer. While final answer varies from task to task, which truly causes non-uniform output formats, finding the answer-related text is a common action among all tasks. Traditional methods regard both the steps as a whole. In this paper, we focus on finding the answer-related substring $Xs = <X_i, X_i+1, X_i+2, ... X_j> (1 <= i < j <= n)$ from paragraph text $X$. For example, given sentence UTF8gkai“远端胃切除标本：小弯长11.5cm，大弯长17.0cm。距上切端6.0cm、下切端8.0cm\" (Distal gastrectomy specimen: measuring 11.5cm in length along the lesser curvature, 17.0cm in length along the greater curvature; 6.0cm from the proximal resection margin, and 8.0cm from the distal resection margin) and query UTF8gkai“上切缘距离\"(proximal resection margin), the answer should be 6.0cm which is located in original text from index 32 to 37. With such definition, it unifies the output format of CTS tasks and therefore make the training data shareable, in order to reduce the training data quantity requirement.", "Since BERT BIBREF26 has already demonstrated the usefulness of shared model, we suppose extracting commonality of this problem and unifying the output format will make the model more powerful than dedicated model and meanwhile, for a specific clinical task, use the data for other tasks to supplement the training data." ], [ "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word." ], [ "For any clinical free-text paragraph $X$ and query $Q$, contextualized representation is to generate the encoded vector of both of them. Here we use pre-trained language model BERT-base BIBREF26 model to capture contextual information.", "The text input is constructed as `[CLS] $Q$ [SEP] $X$ [SEP]'. For Chinese sentence, each word in this input will be mapped to a pre-trained embedding $e_i$. To tell the model $Q$ and $X$ is two different sentence, a sentence type input is generated which is a binary label sequence to denote what sentence each character in the input belongs to. Positional encoding and mask matrix is also constructed automatically to bring in absolute position information and eliminate the impact of zero padding respectively. Then a hidden vector $V_s$ which contains both query and text information is generated through BERT-base model." ], [ "Since BERT is trained on general corpus, its performance on biomedical domain can be improved by introducing biomedical domain-specific features. In this paper, we introduce clinical named entity information into the model.", "The CNER task aims to identify and classify important clinical terms such as diseases, symptoms, treatments, exams, and body parts from Chinese EHRs. It can be regarded as a sequence labeling task. A CNER model typically outputs a sequence of tags. Each character of the original sentence will be tagged a label following a tag scheme. In this paper we recognize the entities by the model of our previous work BIBREF12 but trained on another corpus which has 44 entity types including operations, numbers, unit words, examinations, symptoms, negative words, etc. An illustrative example of named entity information sequence is demonstrated in Table TABREF2. In Table TABREF2, UTF8gkai“远端胃切除\" is tagged as an operation, `11.5' is a number word and `cm' is an unit word. The named entity tag sequence is organized in one-hot type. We denote the sequence for clinical sentence and query term as $I_{nt}$ and $I_{nq}$, respectively." ], [ "There are two ways to integrate two named entity information vectors $I_{nt}$ and $I_{nq}$ or hidden contextualized representation $V_s$ and named entity information $I_n$, where $I_n = [I_{nt}; I_{nq}]$. The first one is to concatenate them together because they have sequence output with a common dimension. The second one is to transform them into a new hidden representation. For the concatenation method, the integrated representation is described as follows.", "While for the transformation method, we use multi-head attention BIBREF30 to encode the two vectors. It can be defined as follows where $h$ is the number of heads and $W_o$ is used to projects back the dimension of concatenated matrix.", "$Attention$ denotes the traditional attention and it can be defined as follows.", "where $d_k$ is the length of hidden vector." ], [ "The final step is to use integrated representation $H_i$ to predict the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word. We use a feed forward network (FFN) to compress and calculate the score of each word $H_f$ which makes the dimension to $\\left\\langle l_s, 2\\right\\rangle $ where $l_s$ denotes the length of sequence.", "Then we permute the two dimensions for softmax calculation. The calculation process of loss function can be defined as followed.", "where $O_s = softmax(permute(H_f)_0)$ denotes the probability score of each word to be the start word and similarly $O_e = softmax(permute(H_f)_1)$ denotes the end. $y_s$ and $y_e$ denotes the true answer of the output for start word and end word respectively." ], [ "Two-stage training mechanism is previously applied on bilinear model in fine-grained visual recognition BIBREF31, BIBREF32, BIBREF33. Two CNNs are deployed in the model. One is trained at first for coarse-graind features while freezing the parameter of the other. Then unfreeze the other one and train the entire model in a low learning rate for fetching fine-grained features.", "Inspired by this and due to the large amount of parameters in BERT model, to speed up the training process, we fine tune the BERT model with new prediction layer first to achieve a better contextualized representation performance. Then we deploy the proposed model and load the fine tuned BERT weights, attach named entity information layers and retrain the model." ], [ "In this section, we devote to experimentally evaluating our proposed task and approach. The best results in tables are in bold." ], [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.", "In the following experiments, two widely-used performance measures (i.e., EM-score BIBREF34 and (macro-averaged) F$_1$-score BIBREF35) are used to evaluate the methods. The Exact Match (EM-score) metric measures the percentage of predictions that match any one of the ground truth answers exactly. The F$_1$-score metric is a looser metric measures the average overlap between the prediction and ground truth answer." ], [ "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts." ], [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.", "Table TABREF23 indicates that our proposed model achieved the best performance both in EM-score and F$_1$-score with EM-score of 91.84% and F$_1$-score of 93.75%. QANet outperformed BERT-Base with 3.56% score in F$_1$-score but underperformed it with 0.75% score in EM-score. Compared with BERT-Base, our model led to a 5.64% performance improvement in EM-score and 3.69% in F$_1$-score. Although our model didn't outperform much with QANet in F$_1$-score (only 0.13%), our model significantly outperformed it with 6.39% score in EM-score." ], [ "To further investigate the effects of named entity information and two-stage training mechanism for our model, we apply ablation analysis to see the improvement brought by each of them, where $\\times $ refers to removing that part from our model.", "As demonstrated in Table TABREF25, with named entity information enabled, two-stage training mechanism improved the result by 4.36% in EM-score and 3.8% in F$_1$-score. Without two-stage training mechanism, named entity information led to an improvement by 1.28% in EM-score but it also led to a weak deterioration by 0.12% in F$_1$-score. With both of them enabled, our proposed model achieved a 5.64% score improvement in EM-score and a 3.69% score improvement in F$_1$-score. The experimental results show that both named entity information and two-stage training mechanism are helpful to our model." ], [ "There are two methods to integrate named entity information into existing model, we experimentally compare these two integration methods. As named entity recognition has been applied on both pathology report text and query text, there will be two integration here. One is for two named entity information and the other is for contextualized representation and integrated named entity information. For multi-head attention BIBREF30, we set heads number $h = 16$ with 256-dimension hidden vector size for each head.", "From Table TABREF27, we can observe that applying concatenation on both periods achieved the best performance on both EM-score and F$_1$-score. Unfortunately, applying multi-head attention on both period one and period two can not reach convergence in our experiments. This probably because it makes the model too complex to train. The difference on other two methods are the order of concatenation and multi-head attention. Applying multi-head attention on two named entity information $I_{nt}$ and $I_{nq}$ first achieved a better performance with 89.87% in EM-score and 92.88% in F$_1$-score. Applying Concatenation first can only achieve 80.74% in EM-score and 84.42% in F$_1$-score. This is probably due to the processing depth of hidden vectors and dataset size. BERT's output has been modified after many layers but named entity information representation is very close to input. With big amount of parameters in multi-head attention, it requires massive training to find out the optimal parameters. However, our dataset is significantly smaller than what pre-trained BERT uses. This probably can also explain why applying multi-head attention method on both periods can not converge.", "Although Table TABREF27 shows the best integration method is concatenation, multi-head attention still has great potential. Due to the lack of computational resources, our experiment fixed the head number and hidden vector size. However, tuning these hyper parameters may have impact on the result. Tuning integration method and try to utilize larger datasets may give help to improving the performance." ], [ "To investigate how shared task and shared model can benefit, we split our dataset by query types, train our proposed model with different datasets and demonstrate their performance on different datasets. Firstly, we investigate the performance on model without two-stage training and named entity information.", "As indicated in Table TABREF30, The model trained by mixed data outperforms 2 of the 3 original tasks in EM-score with 81.55% for proximal resection margin and 86.85% for distal resection margin. The performance on tumor size declined by 1.57% score in EM-score and 3.14% score in F$_1$-score but they were still above 90%. 0.69% and 0.37% score improvement in EM-score was brought by shared model for proximal and distal resection margin prediction. Meanwhile F$_1$-score for those two tasks declined 3.11% and 0.77% score.", "Then we investigate the performance on model with two-stage training and named entity information. In this experiment, pre-training process only use the specific dataset not the mixed data. From Table TABREF31 we can observe that the performance on proximal and distal resection margin achieved the best performance on both EM-score and F$_1$-score. Compared with Table TABREF30, the best performance on proximal resection margin improved by 6.9% in EM-score and 7.94% in F$_1$-score. Meanwhile, the best performance on distal resection margin improved by 5.56% in EM-score and 6.32% in F$_1$-score. Other performances also usually improved a lot. This proves the usefulness of two-stage training and named entity information as well.", "Lastly, we fine tune the model for each task with a pre-trained parameter. Table TABREF32 summarizes the result. (Add some explanations for the Table TABREF32). Comparing Table TABREF32 with Table TABREF31, using mixed-data pre-trained parameters can significantly improve the model performance than task-specific data trained model. Except tumor size, the result was improved by 0.52% score in EM-score, 1.39% score in F$_1$-score for proximal resection margin and 2.6% score in EM-score, 2.96% score in F$_1$-score for distal resection margin. This proves mixed-data pre-trained parameters can lead to a great benefit for specific task. Meanwhile, the model performance on other tasks which are not trained in the final stage was also improved from around 0 to 60 or 70 percent. This proves that there is commonality between different tasks and our proposed QA-CTS task make this learnable. In conclusion, to achieve the best performance for a specific dataset, pre-training the model in multiple datasets and then fine tuning the model on the specific dataset is the best way." ], [ "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset." ], [ "We would like to thank Ting Li and Xizhou Hong (Ruijin Hospital) who have helped us very much in data fetching and data cleansing. This work is supported by the National Key R&D Program of China for “Precision Medical Research\" (No. 2018YFC0910500)." ] ], "section_name": [ "Introduction", "Related Work ::: Clinical Text Structuring", "Related Work ::: Pre-trained Language Model", "Question Answering based Clinical Text Structuring", "The Proposed Model for QA-CTS Task", "The Proposed Model for QA-CTS Task ::: Contextualized Representation of Sentence Text and Query Text", "The Proposed Model for QA-CTS Task ::: Clinical Named Entity Information", "The Proposed Model for QA-CTS Task ::: Integration Method", "The Proposed Model for QA-CTS Task ::: Final Prediction", "The Proposed Model for QA-CTS Task ::: Two-Stage Training Mechanism", "Experimental Studies", "Experimental Studies ::: Dataset and Evaluation Metrics", "Experimental Studies ::: Experimental Settings", "Experimental Studies ::: Comparison with State-of-the-art Methods", "Experimental Studies ::: Ablation Analysis", "Experimental Studies ::: Comparisons Between Two Integration Methods", "Experimental Studies ::: Data Integration Analysis", "Conclusion", "Acknowledgment" ] }

{ "answers": [ { "annotation_id": [ "0ab604dbe114dba174da645cc06a713e12a1fd9d", "1f1495d06d0abe86ee52124ec9f2f0b25a536147" ], "answer": [ { "evidence": [ "To implement deep neural network models, we utilize the Keras library BIBREF36 with TensorFlow BIBREF37 backend. Each model is run on a single NVIDIA GeForce GTX 1080 Ti GPU. The models are trained by Adam optimization algorithm BIBREF38 whose parameters are the same as the default settings except for learning rate set to $5\\times 10^{-5}$. Batch size is set to 3 or 4 due to the lack of graphical memory. We select BERT-base as the pre-trained language model in this paper. Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts." ], "extractive_spans": [ "Chinese general corpus" ], "free_form_answer": "", "highlighted_evidence": [ "Due to the high cost of pre-training BERT language model, we directly adopt parameters pre-trained by Google in Chinese general corpus. The named entity recognition is applied on both pathology report texts and query texts." ], "unanswerable": false, "yes_no": null }, { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": null } ], "worker_id": [ "a0b403873302db7cada39008f04d01155ef68f4f", "258ee4069f740c400c0049a2580945a1cc7f044c" ] }, { "annotation_id": [ "0de2087bf0e46b14042de2a6e707bbf544a04556", "c14d9acff1d3e6f47901e7104a7f01a10a727050" ], "answer": [ { "evidence": [ "Experimental Studies ::: Comparison with State-of-the-art Methods", "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23." ], "extractive_spans": [ "BERT-Base", "QANet" ], "free_form_answer": "", "highlighted_evidence": [ "Experimental Studies ::: Comparison with State-of-the-art Methods\nSince BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large." ], "unanswerable": false, "yes_no": null }, { "evidence": [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23.", "FLOAT SELECTED: TABLE III COMPARATIVE RESULTS BETWEEN BERT AND OUR PROPOSED MODEL" ], "extractive_spans": [ "QANet BIBREF39", "BERT-Base BIBREF26" ], "free_form_answer": "", "highlighted_evidence": [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. ", "FLOAT SELECTED: TABLE III COMPARATIVE RESULTS BETWEEN BERT AND OUR PROPOSED MODEL" ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "258ee4069f740c400c0049a2580945a1cc7f044c", "a0b403873302db7cada39008f04d01155ef68f4f" ] }, { "annotation_id": [ "6d56080358bb7f22dd764934ffcd6d4e93fef0b2", "da233cce57e642941da2446d3e053349c2ab1a15" ], "answer": [ { "evidence": [ "FLOAT SELECTED: Fig. 1. An illustrative example of QA-CTS task.", "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows." ], "extractive_spans": [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained.", "Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. " ], "free_form_answer": "", "highlighted_evidence": [ "FLOAT SELECTED: Fig. 1. An illustrative example of QA-CTS task.", "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. " ], "unanswerable": false, "yes_no": null }, { "evidence": [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly. In addition, clinical text often contains abundant healthcare information. CTS is able to provide large-scale extracted structured data for enormous down-stream clinical researches.", "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size). Researchers have to construct different models for it, which is already costly, and hence it calls for a lot of labeled data for each model. Moreover, labeling necessary amount of data for training neural network requires expensive labor cost. To handle it, researchers turn to some rule-based structuring methods which often have lower labor cost.", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows." ], "extractive_spans": [], "free_form_answer": "CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text.", "highlighted_evidence": [ "Clinical text structuring (CTS) is a critical task for fetching medical research data from electronic health records (EHRs), where structural patient medical data, such as whether the patient has specific symptoms, diseases, or what the tumor size is, how far from the tumor is cut at during the surgery, or what the specific laboratory test result is, are obtained. It is important to extract structured data from clinical text because bio-medical systems or bio-medical researches greatly rely on structured data but they cannot obtain them directly.", "However, end-to-end CTS is a very challenging task. Different CTS tasks often have non-uniform output formats, such as specific-class classifications (e.g. tumor stage), strings in the original text (e.g. result for a laboratory test) and inferred values from part of the original text (e.g. calculated tumor size).", "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "a0b403873302db7cada39008f04d01155ef68f4f", "258ee4069f740c400c0049a2580945a1cc7f044c" ] }, { "annotation_id": [ "7138d812ea70084e7610e5a2422039da1404afd7", "b732d5561babcf37393ebf6cbb051d04b0b66bd5" ], "answer": [ { "evidence": [ "To reduce the pipeline depth and break the barrier of non-uniform output formats, we present a question answering based clinical text structuring (QA-CTS) task (see Fig. FIGREF1). Unlike the traditional CTS task, our QA-CTS task aims to discover the most related text from original paragraph text. For some cases, it is already the final answer in deed (e.g., extracting sub-string). While for other cases, it needs several steps to obtain the final answer, such as entity names conversion and negative words recognition. Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. The main contributions of this work can be summarized as follows.", "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20.", "In this paper, we present a question answering based clinical text structuring (QA-CTS) task, which unifies different clinical text structuring tasks and utilize different datasets. A novel model is also proposed to integrate named entity information into a pre-trained language model and adapt it to QA-CTS task. Initially, sequential results of named entity recognition on both paragraph and query texts are integrated together. Contextualized representation on both paragraph and query texts are transformed by a pre-trained language model. Then, the integrated named entity information and contextualized representation are integrated together and fed into a feed forward network for final prediction. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks. The shared task and shared model introduced by QA-CTS task has also been proved to be useful for improving the performance on most of the task-specific datasets. In conclusion, the best way to achieve the best performance for a specific dataset is to pre-train the model in multiple datasets and then fine tune it on the specific dataset." ], "extractive_spans": [ " three types of questions, namely tumor size, proximal resection margin and distal resection margin" ], "free_form_answer": "", "highlighted_evidence": [ "Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data.", "All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. ", "Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks." ], "unanswerable": false, "yes_no": null }, { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": null } ], "worker_id": [ "a0b403873302db7cada39008f04d01155ef68f4f", "258ee4069f740c400c0049a2580945a1cc7f044c" ] }, { "annotation_id": [ "1d4d4965fd44fefbfed0b3267ef5875572994b66" ], "answer": [ { "evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ], "extractive_spans": [], "free_form_answer": "the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences ", "highlighted_evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "fa716cd87ce6fd6905e2f23f09b262e90413167f" ] }, { "annotation_id": [ "229cc59d1545c9e8f47d43053465e2dfd1b763cc" ], "answer": [ { "evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ], "extractive_spans": [], "free_form_answer": "2,714 ", "highlighted_evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "fa716cd87ce6fd6905e2f23f09b262e90413167f" ] }, { "annotation_id": [ "e2fe2a3438f28758724d992502a44615051eda90" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": null } ], "worker_id": [ "fa716cd87ce6fd6905e2f23f09b262e90413167f" ] }, { "annotation_id": [ "2a73264b743b6dd183c200f7dcd04aed4029f015" ], "answer": [ { "evidence": [], "extractive_spans": [], "free_form_answer": "", "highlighted_evidence": [], "unanswerable": true, "yes_no": null } ], "worker_id": [ "fa716cd87ce6fd6905e2f23f09b262e90413167f" ] }, { "annotation_id": [ "5f125408e657282669f90a1866d8227c0f94332e" ], "answer": [ { "evidence": [ "We first present a question answering based clinical text structuring (QA-CTS) task, which unifies different specific tasks and make dataset shareable. We also propose an effective model to integrate clinical named entity information into pre-trained language model.", "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text. Here we define this calculation problem as a classification for each word to be the start or end word." ], "extractive_spans": [ "integrate clinical named entity information into pre-trained language model" ], "free_form_answer": "", "highlighted_evidence": [ "We also propose an effective model to integrate clinical named entity information into pre-trained language model.", "In this section, we present an effective model for the question answering based clinical text structuring (QA-CTS). As shown in Fig. FIGREF8, paragraph text $X$ is first passed to a clinical named entity recognition (CNER) model BIBREF12 to capture named entity information and obtain one-hot CNER output tagging sequence for query text $I_{nq}$ and paragraph text $I_{nt}$ with BIEOS (Begin, Inside, End, Outside, Single) tag scheme. $I_{nq}$ and $I_{nt}$ are then integrated together into $I_n$. Meanwhile, the paragraph text $X$ and query text $Q$ are organized and passed to contextualized representation model which is pre-trained language model BERT BIBREF26 here to obtain the contextualized representation vector $V_s$ of both text and query. Afterwards, $V_s$ and $I_n$ are integrated together and fed into a feed forward network to calculate the start and end index of answer-related text." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "e70d8110563d53282f1a26e823d27e6f235772db" ] }, { "annotation_id": [ "24c7023a5221b509d34dd6703d6e0607b2777e78" ], "answer": [ { "evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ], "extractive_spans": [ "17,833 sentences, 826,987 characters and 2,714 question-answer pairs" ], "free_form_answer": "", "highlighted_evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. " ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "e70d8110563d53282f1a26e823d27e6f235772db" ] }, { "annotation_id": [ "d046d9ea83c5ffe607465e2fbc8817131c11e037" ], "answer": [ { "evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. These annotated instances have been partitioned into 1,899 training instances (12,412 sentences) and 815 test instances (5,421 sentences). Each instance has one or several sentences. Detailed statistics of different types of entities are listed in Table TABREF20." ], "extractive_spans": [ "17,833 sentences, 826,987 characters and 2,714 question-answer pairs" ], "free_form_answer": "", "highlighted_evidence": [ "Our dataset is annotated based on Chinese pathology reports provided by the Department of Gastrointestinal Surgery, Ruijin Hospital. It contains 17,833 sentences, 826,987 characters and 2,714 question-answer pairs." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "e70d8110563d53282f1a26e823d27e6f235772db" ] }, { "annotation_id": [ "b3a3d6e707a67bab827053b40e446f30e416887f" ], "answer": [ { "evidence": [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large. Prediction layer is attached at the end of the original BERT-Base model and we fine tune it on our dataset. In this section, the named entity integration method is chosen to pure concatenation (Concatenate the named entity information on pathology report text and query text first and then concatenate contextualized representation and concatenated named entity information). Comparative results are summarized in Table TABREF23." ], "extractive_spans": [ "state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26" ], "free_form_answer": "", "highlighted_evidence": [ "Since BERT has already achieved the state-of-the-art performance of question-answering, in this section we compare our proposed model with state-of-the-art question answering models (i.e. QANet BIBREF39) and BERT-Base BIBREF26. As BERT has two versions: BERT-Base and BERT-Large, due to the lack of computational resource, we can only compare with BERT-Base model instead of BERT-Large." ], "unanswerable": false, "yes_no": null } ], "worker_id": [ "e70d8110563d53282f1a26e823d27e6f235772db" ] } ], "nlp_background": [ "infinity", "infinity", "infinity", "infinity", "five", "five", "five", "five", "zero", "zero", "zero", "zero" ], "paper_read": [ "no", "no", "no", "no", "no", "no", "no", "no", "no", "no", "no", "no" ], "question": [ "What data is the language model pretrained on?", "What baselines is the proposed model compared against?", "How is the clinical text structuring task defined?", "What are the specific tasks being unified?", "Is all text in this dataset a question, or are there unrelated sentences in between questions?", "How many questions are in the dataset?", "What is the perWhat are the tasks evaluated?", "Are there privacy concerns with clinical data?", "How they introduce domain-specific features into pre-trained language model?", "How big is QA-CTS task dataset?", "How big is dataset of pathology reports collected from Ruijing Hospital?", "What are strong baseline models in specific tasks?" ], "question_id": [ "71a7153e12879defa186bfb6dbafe79c74265e10", "85d1831c28d3c19c84472589a252e28e9884500f", "1959e0ebc21fafdf1dd20c6ea054161ba7446f61", "77cf4379106463b6ebcb5eb8fa5bb25450fa5fb8", "06095a4dee77e9a570837b35fc38e77228664f91", "19c9cfbc4f29104200393e848b7b9be41913a7ac", "6743c1dd7764fc652cfe2ea29097ea09b5544bc3", "14323046220b2aea8f15fba86819cbccc389ed8b", "08a5f8d36298b57f6a4fcb4b6ae5796dc5d944a4", "975a4ac9773a4af551142c324b64a0858670d06e", "326e08a0f5753b90622902bd4a9c94849a24b773", "bd78483a746fda4805a7678286f82d9621bc45cf" ], "question_writer": [ "ecca0cede84b7af8a918852311d36346b07f0668", "ecca0cede84b7af8a918852311d36346b07f0668", "ecca0cede84b7af8a918852311d36346b07f0668", "ecca0cede84b7af8a918852311d36346b07f0668", "2a18a3656984d04249f100633e4c1003417a2255", "2a18a3656984d04249f100633e4c1003417a2255", "2a18a3656984d04249f100633e4c1003417a2255", "2a18a3656984d04249f100633e4c1003417a2255", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c", "258ee4069f740c400c0049a2580945a1cc7f044c" ], "search_query": [ "question answering", "question answering", "question answering", "question answering", "Question Answering", "Question Answering", "Question Answering", "Question Answering", "", "", "", "" ], "topic_background": [ "research", "research", "research", "research", "familiar", "familiar", "familiar", "familiar", "unfamiliar", "unfamiliar", "unfamiliar", "unfamiliar" ] }

{ "caption": [ "Fig. 1. An illustrative example of QA-CTS task.", "TABLE I AN ILLUSTRATIVE EXAMPLE OF NAMED ENTITY FEATURE TAGS", "Fig. 2. The architecture of our proposed model for QA-CTS task", "TABLE II STATISTICS OF DIFFERENT TYPES OF QUESTION ANSWER INSTANCES", "TABLE V COMPARATIVE RESULTS FOR DIFFERENT INTEGRATION METHOD OF OUR PROPOSED MODEL", "TABLE III COMPARATIVE RESULTS BETWEEN BERT AND OUR PROPOSED MODEL", "TABLE VI COMPARATIVE RESULTS FOR DATA INTEGRATION ANALYSIS (WITHOUT TWO-STAGE TRAINING AND NAMED ENTITY INFORMATION)", "TABLE VII COMPARATIVE RESULTS FOR DATA INTEGRATION ANALYSIS (WITH TWO-STAGE TRAINING AND NAMED ENTITY INFORMATION)", "TABLE VIII COMPARATIVE RESULTS FOR DATA INTEGRATION ANALYSIS (USING MIXED-DATA PRE-TRAINED PARAMETERS)" ], "file": [ "1-Figure1-1.png", "2-TableI-1.png", "3-Figure2-1.png", "4-TableII-1.png", "5-TableV-1.png", "5-TableIII-1.png", "6-TableVI-1.png", "6-TableVII-1.png", "6-TableVIII-1.png" ] }

[ "How is the clinical text structuring task defined?", "Is all text in this dataset a question, or are there unrelated sentences in between questions?", "How many questions are in the dataset?" ]

[ [ "1908.06606-Introduction-0", "1908.06606-1-Figure1-1.png", "1908.06606-Introduction-1", "1908.06606-Introduction-3" ], [ "1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0" ], [ "1908.06606-Experimental Studies ::: Dataset and Evaluation Metrics-0" ] ]

[ "CTS is extracting structural data from medical research data (unstructured). Authors define QA-CTS task that aims to discover most related text from original text.", "the dataset consists of pathology reports including sentences and questions and answers about tumor size and resection margins so it does include additional sentences ", "2,714 " ]

{ "paragraphs": [ [ "Deep learning has unquestionably advanced the state of the art in many natural language processing tasks, from syntactic dependency parsing BIBREF0 to named-entity recognition BIBREF1 to machine translation BIBREF2 . The same certainly applies to language modeling, where recent advances in neural language models (NLMs) have led to dramatically better approaches as measured using standard metrics such as perplexity BIBREF3 , BIBREF4 .", "Specifically focused on language modeling, this paper examines an issue that to our knowledge has not been explored: advances in neural language models have come at a significant cost in terms of increased computational complexity. Computing the probability of a token sequence using non-neural techniques requires a number of phrase lookups and perhaps a few arithmetic operations, whereas model inference with NLMs require large matrix multiplications consuming perhaps millions of floating point operations (FLOPs). These performance tradeoffs are worth discussing.", "In truth, language models exist in a quality–performance tradeoff space. As model quality increases (e.g., lower perplexity), performance as measured in terms of energy consumption, query latency, etc. tends to decrease. For applications primarily running in the cloud—say, machine translation—practitioners often solely optimize for the lowest perplexity. This is because such applications are embarrassingly parallel and hence trivial to scale in a data center environment.", "There are, however, applications of NLMs that require less one-sided optimizations. On mobile devices such as smartphones and tablets, for example, NLMs may be integrated into software keyboards for next-word prediction, allowing much faster text entry. Popular Android apps that enthusiastically tout this technology include SwiftKey and Swype. The greater computational costs of NLMs lead to higher energy usage in model inference, translating into shorter battery life.", "In this paper, we examine the quality–performance tradeoff in the shift from non-neural to neural language models. In particular, we compare Kneser–Ney smoothing, widely accepted as the state of the art prior to NLMs, to the best NLMs today. The decrease in perplexity on standard datasets has been well documented BIBREF3 , but to our knowledge no one has examined the performances tradeoffs. With deployment on a mobile device in mind, we evaluate energy usage and inference latency on a Raspberry Pi (which shares the same ARM architecture as nearly all smartphones today). We find that a 2.5 $\\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\\times $ longer and requires 32 $\\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. Nevertheless, NLP engineers should arguably keep these tradeoffs in mind when selecting a particular operating point." ], [ " BIBREF3 evaluate recent neural language models; however, their focus is not on the computational footprint of each model, but rather the perplexity. To further reduce perplexity, many neural language model extensions exist, such as continuous cache pointer BIBREF5 and mixture of softmaxes BIBREF6 . Since our focus is on comparing “core” neural and non-neural approaches, we disregard these extra optimizations techniques in all of our models.", "Other work focus on designing lightweight models for resource-efficient inference on mobile devices. BIBREF7 explore LSTMs BIBREF8 with binary weights for language modeling; BIBREF9 examine shallow feedforward neural networks for natural language processing.", "AWD-LSTM. BIBREF4 show that a simple three-layer LSTM, with proper regularization and optimization techniques, can achieve state of the art on various language modeling datasets, surpassing more complex models. Specifically, BIBREF4 apply randomized backpropagation through time, variational dropout, activation regularization, embedding dropout, and temporal activation regularization. A novel scheduler for optimization, non-monotonically triggered ASGD (NT-ASGD) is also introduced. BIBREF4 name their three-layer LSTM model trained with such tricks, “AWD-LSTM.”", "Quasi-Recurrent Neural Networks. Quasi-recurrent neural networks (QRNNs; BIBREF10 ) achieve current state of the art in word-level language modeling BIBREF11 . A quasi-recurrent layer comprises two separate parts: a convolution layer with three weights, and a recurrent pooling layer. Given an input $\\mathbf {X} \\in \\mathbb {R}^{k \\times n}$ , the convolution layer is $\n\\mathbf {Z} = \\tanh (\\mathbf {W}_z \\cdot \\mathbf {X})\\\\\n\\mathbf {F} = \\sigma (\\mathbf {W}_f \\cdot \\mathbf {X})\\\\\n\\mathbf {O} = \\sigma (\\mathbf {W}_o \\cdot \\mathbf {X})\n$ ", "where $\\sigma $ denotes the sigmoid function, $\\cdot $ represents masked convolution across time, and $\\mathbf {W}_{\\lbrace z, f, o\\rbrace } \\in \\mathbb {R}^{m \\times k \\times r}$ are convolution weights with $k$ input channels, $m$ output channels, and a window size of $r$ . In the recurrent pooling layer, the convolution outputs are combined sequentially: $\n\\mathbf {c}_t &= \\mathbf {f}_t \\odot \\mathbf {c}_{t-1} + (1 -\n\\mathbf {f}_t) \\odot \\mathbf {z}_t\\\\\n\\mathbf {h}_t &= \\mathbf {o}_t \\odot \\mathbf {c}_t\n$ ", "Multiple QRNN layers can be stacked for deeper hierarchical representation, with the output $\\mathbf {h}_{1:t}$ being fed as the input into the subsequent layer: In language modeling, a four-layer QRNN is a standard architecture BIBREF11 .", "Perplexity–Recall Scale. Word-level perplexity does not have a strictly monotonic relationship with recall-at- $k$ , the fraction of top $k$ predictions that contain the correct word. A given R@ $k$ imposes a weak minimum perplexity constraint—there are many free parameters that allow for large variability in the perplexity given a certain R@ $k$ . Consider the corpus, “choo choo train,” with an associated unigram model $P(\\text{``choo''}) = 0.1$ , $P(\\text{``train''}) = 0.9$ , resulting in an R@1 of $1/3$ and perplexity of $4.8$ . Clearly, R@1 $ =1/3$ for all $P(\\text{``choo''}) \\le 0.5$ ; thus, perplexity can drop as low as 2 without affecting recall." ], [ "We conducted our experiments on Penn Treebank (PTB; BIBREF12 ) and WikiText-103 (WT103; BIBREF13 ). Preprocessed by BIBREF14 , PTB contains 887K tokens for training, 70K for validation, and 78K for test, with a vocabulary size of 10,000. On the other hand, WT103 comprises 103 million tokens for training, 217K for validation, and 245K for test, spanning a vocabulary of 267K unique tokens.", "For the neural language model, we used a four-layer QRNN BIBREF10 , which achieves state-of-the-art results on a variety of datasets, such as WT103 BIBREF11 and PTB. To compare against more common LSTM architectures, we also evaluated AWD-LSTM BIBREF4 on PTB. For the non-neural approach, we used a standard five-gram model with modified Kneser-Ney smoothing BIBREF15 , as explored in BIBREF16 on PTB. We denote the QRNN models for PTB and WT103 as ptb-qrnn and wt103-qrnn, respectively.", "For each model, we examined word-level perplexity, R@3 in next-word prediction, latency (ms/q), and energy usage (mJ/q). To explore the perplexity–recall relationship, we collected individual perplexity and recall statistics for each sentence in the test set." ], [ "The QRNN models followed the exact training procedure and architecture delineated in the official codebase from BIBREF11 . For ptb-qrnn, we trained the model for 550 epochs using NT-ASGD BIBREF4 , then finetuned for 300 epochs using ASGD BIBREF17 , all with a learning rate of 30 throughout. For wt103-qrnn, we followed BIBREF11 and trained the QRNN for 14 epochs, using the Adam optimizer with a learning rate of $10^{-3}$ . We also applied regularization techniques from BIBREF4 ; all the specific hyperparameters are the same as those in the repository. Our model architecture consists of 400-dimensional tied embedding weights BIBREF18 and four QRNN layers, with 1550 hidden units per layer on PTB and 2500 per layer on WT103. Both QRNN models have window sizes of $r=2$ for the first layer and $r=1$ for the rest.", "For the KN-5 model, we trained an off-the-shelf five-gram model using the popular SRILM toolkit BIBREF19 . We did not specify any special hyperparameters." ], [ "We trained the QRNNs with PyTorch (0.4.0; commit 1807bac) on a Titan V GPU. To evaluate the models under a resource-constrained environment, we deployed them on a Raspberry Pi 3 (Model B) running Raspbian Stretch (4.9.41-v7+). The Raspberry Pi (RPi) is not only a standard platform, but also a close surrogate to mobile phones, using the same Cortex-A7 in many phones. We then transferred the trained models to the RPi, using the same frameworks for evaluation. We plugged the RPi into a Watts Up Pro meter, a power meter that can be read programatically over USB at a frequency of 1 Hz. For the QRNNs, we used the first 350 words of the test set, and averaged the ms/query and mJ/query. For KN-5, we used the entire test set for evaluation, since the latency was much lower. To adjust for the base power load, we subtracted idle power draw from energy usage.", "For a different perspective, we further evaluated all the models under a desktop environment, using an i7-4790k CPU and Titan V GPU. Because the base power load for powering a desktop is much higher than running neural language models, we collected only latency statistics. We used the entire test set, since the QRNN runs quickly.", "In addition to energy and latency, another consideration for the NLP developer selecting an operating point is the cost of underlying hardware. For our setup, the RPi costs $35 USD, the CPU costs $350 USD, and the GPU costs $3000 USD." ], [ "To demonstrate the effectiveness of the QRNN models, we present the results of past and current state-of-the-art neural language models in Table 1 ; we report the Skip- and AWD-LSTM results as seen in the original papers, while we report our QRNN results. Skip LSTM denotes the four-layer Skip LSTM in BIBREF3 . BIBREF20 focus on Hebbian softmax, a model extension technique—Rae-LSTM refers to their base LSTM model without any extensions. In our results, KN-5 refers to the traditional five-gram model with modified Kneser-Ney smoothing, and AWD is shorthand for AWD-LSTM.", "Perplexity–recall scale. In Figure 1 , using KN-5 as the model, we plot the log perplexity (cross entropy) and R@3 error ( $1 - \\text{R@3}$ ) for every sentence in PTB and WT103. The horizontal clusters arise from multiple perplexity points representing the same R@3 value, as explained in Section \"Infrastructure\" . We also observe that the perplexity–recall scale is non-linear—instead, log perplexity appears to have a moderate linear relationship with R@3 error on PTB ( $r=0.85$ ), and an even stronger relationship on WT103 ( $r=0.94$ ). This is partially explained by WT103 having much longer sentences, and thus less noisy statistics.", "From Figure 1 , we find that QRNN models yield strongly linear log perplexity–recall plots as well, where $r=0.88$ and $r=0.93$ for PTB and WT103, respectively. Note that, due to the improved model quality over KN-5, the point clouds are shifted downward compared to Figure 1 . We conclude that log perplexity, or cross entropy, provides a more human-understandable indicator of R@3 than perplexity does. Overall, these findings agree with those from BIBREF21 , which explores the log perplexity–word error rate scale in language modeling for speech recognition.", "Quality–performance tradeoff. In Table 2 , from left to right, we report perplexity results on the validation and test sets, R@3 on test, and finally per-query latency and energy usage. On the RPi, KN-5 is both fast and power-efficient to run, using only about 7 ms/query and 6 mJ/query for PTB (Table 2 , row 1), and 264 ms/q and 229 mJ/q on WT103 (row 5). Taking 220 ms/query and consuming 300 mJ/query, AWD-LSTM and ptb-qrnn are still viable for mobile phones: The modern smartphone holds upwards of 10,000 joules BIBREF22 , and the latency is within usability standards BIBREF23 . Nevertheless, the models are still 49 $\\times $ slower and 32 $\\times $ more power-hungry than KN-5. The wt103-qrnn model is completely unusable on phones, taking over 1.2 seconds per next-word prediction. Neural models achieve perplexity drops of 60–80% and R@3 increases of 22–34%, but these improvements come at a much higher cost in latency and energy usage.", "In Table 2 (last two columns), the desktop yields very different results: the neural models on PTB (rows 2–3) are 9 $\\times $ slower than KN-5, but the absolute latency is only 8 ms/q, which is still much faster than what humans perceive as instantaneous BIBREF23 . If a high-end commodity GPU is available, then the models are only twice as slow as KN-5 is. From row 5, even better results are noted with wt103-qrnn: On the CPU, the QRNN is only 60% slower than KN-5 is, while the model is faster by 11 $\\times $ on a GPU. These results suggest that, if only latency is considered under a commodity desktop environment, the QRNN model is humanly indistinguishable from the KN-5 model, even without using GPU acceleration." ], [ "In the present work, we describe and examine the tradeoff space between quality and performance for the task of language modeling. Specifically, we explore the quality–performance tradeoffs between KN-5, a non-neural approach, and AWD-LSTM and QRNN, two neural language models. We find that with decreased perplexity comes vastly increased computational requirements: In one of the NLMs, a perplexity reduction by 2.5 $\\times $ results in a 49 $\\times $ rise in latency and 32 $\\times $ increase in energy usage, when compared to KN-5." ] ], "section_name": [ "Introduction", "Background and Related Work", "Experimental Setup", "Hyperparameters and Training", "Infrastructure", "Results and Discussion", "Conclusion" ] }

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We find that a 2.5 $\\times $ reduction in perplexity on PTB comes at a staggering cost in terms of performance: inference with NLMs takes 49 $\\times $ longer and requires 32 $\\times $ more energy. Furthermore, we find that impressive reductions in perplexity translate into at best modest improvements in next-word prediction, which is arguable a better metric for evaluating software keyboards on a smartphone. The contribution of this paper is the first known elucidation of this quality–performance tradeoff. Note that we refrain from prescriptive recommendations: whether or not a tradeoff is worthwhile depends on the application. 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{ "caption": [ "Table 1: Comparison of neural language models on Penn Treebank and WikiText-103.", "Figure 1: Log perplexity–recall error with KN-5.", "Figure 2: Log perplexity–recall error with QRNN.", "Table 2: Language modeling results on performance and model quality." ], "file": [ "3-Table1-1.png", "4-Figure1-1.png", "4-Figure2-1.png", "4-Table2-1.png" ] }

[ "What aspects have been compared between various language models?" ]

[ [ "1811.00942-Experimental Setup-2" ] ]

[ "Quality measures using perplexity and recall, and performance measured using latency and energy usage. " ]

{"paragraphs":[["Ever since the LIME algorithm BIBREF0 , \"explanation\" techniques focusing on find(...TRUNCATED)

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[ "How many attention layers are there in their model?" ]

[ [ "1907.05664-The Model-0" ] ]

[ "one" ]

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{"caption":["Figure 1: Word embedding semantic quality benchmarks for each bias mitigation method (h(...TRUNCATED)

[ "What are the three measures of bias which are reduced in experiments?" ]

[["1910.14497-Background ::: Geometric Bias Mitigation ::: RIPA-0","1910.14497-4-Table1-1.png","1910(...TRUNCATED)

[ "RIPA, Neighborhood Metric, WEAT" ]

{"paragraphs":[["Analysis of the way court decisions refer to each other provides us with important (...TRUNCATED)

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{"caption":["Figure 1: NLP pipeline including the text segmentation, reference recognition and parsi(...TRUNCATED)

[ "How big is the dataset?" ]

[ [ "2002.02224-Results-0" ] ]

[ "903019 references" ]

{"paragraphs":[["Combat veterans diagnosed with PTSD are substantially more likely to engage in a nu(...TRUNCATED)

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{"caption":["Fig. 1. Overview of our framework","Fig. 2. WordStat dictionary sample","TABLE I DRYHOO(...TRUNCATED)

[ "How is the intensity of the PTSD established?" ]

[["2003.07433-Demographics of Clinically Validated PTSD Assessment Tools-4","2003.07433-Demographics(...TRUNCATED)

[ "defined into four categories from high risk, moderate risk, to low risk" ]

{"paragraphs":[["Sentiment classification is an important task which requires either word level or d(...TRUNCATED)

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{"caption":["Figure 1: Neighbors of word ’sensual’ in Spanish, in bible embedding graph (a) and (...TRUNCATED)

[ "how is quality measured?" ]

[ [ "1904.09678-4-Table1-1.png" ] ]

["Accuracy and the macro-F1 (averaged F1 over positive and negative classes) are used as a measure o(...TRUNCATED)

{"paragraphs":[["Language Identification (LI) is a problem which involves classifying the language b(...TRUNCATED)

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{"caption":["Table 2: Architecture of the 1D-ConvNet model","Fig. 1: Effect of hyperparameter variat(...TRUNCATED)

[ "What is the accuracy reported by state-of-the-art methods?" ]

[ [ "1910.04269-Related Work-6" ] ]

["Answer with content missing: (Table 1)\nPrevious state-of-the art on same dataset: ResNet50 89% (6(...TRUNCATED)