| { |
| "paper_id": "P10-1018", |
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| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:21:08.201832Z" |
| }, |
| "title": "\"Was it good? It was provocative.\" Learning the meaning of scalar adjectives", |
| "authors": [ |
| { |
| "first": "Marie-Catherine", |
| "middle": [], |
| "last": "De Marneffe", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Stanford University Stanford", |
| "location": { |
| "postCode": "94305", |
| "region": "CA" |
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| "email": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
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| "last": "Manning", |
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| "affiliation": { |
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| "institution": "Stanford University Stanford", |
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| "postCode": "94305", |
| "region": "CA" |
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| }, |
| "email": "manning@stanford.edu" |
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| { |
| "first": "Christopher", |
| "middle": [], |
| "last": "Potts", |
| "suffix": "", |
| "affiliation": { |
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| "institution": "Stanford University Stanford", |
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| "postCode": "94305", |
| "region": "CA" |
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| "email": "cgpotts@stanford.edu" |
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| "abstract": "Texts and dialogues often express information indirectly. For instance, speakers' answers to yes/no questions do not always straightforwardly convey a 'yes' or 'no' answer. The intended reply is clear in some cases (Was it good? It was great!) but uncertain in others (Was it acceptable? It was unprecedented.). In this paper, we present methods for interpreting the answers to questions like these which involve scalar modifiers. We show how to ground scalar modifier meaning based on data collected from the Web. We learn scales between modifiers and infer the extent to which a given answer conveys 'yes' or 'no'. To evaluate the methods, we collected examples of question-answer pairs involving scalar modifiers from CNN transcripts and the Dialog Act corpus and use response distributions from Mechanical Turk workers to assess the degree to which each answer conveys 'yes' or 'no'. Our experimental results closely match the Turkers' response data, demonstrating that meanings can be learned from Web data and that such meanings can drive pragmatic inference.", |
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| "paper_id": "P10-1018", |
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| { |
| "text": "Texts and dialogues often express information indirectly. For instance, speakers' answers to yes/no questions do not always straightforwardly convey a 'yes' or 'no' answer. The intended reply is clear in some cases (Was it good? It was great!) but uncertain in others (Was it acceptable? It was unprecedented.). In this paper, we present methods for interpreting the answers to questions like these which involve scalar modifiers. We show how to ground scalar modifier meaning based on data collected from the Web. We learn scales between modifiers and infer the extent to which a given answer conveys 'yes' or 'no'. To evaluate the methods, we collected examples of question-answer pairs involving scalar modifiers from CNN transcripts and the Dialog Act corpus and use response distributions from Mechanical Turk workers to assess the degree to which each answer conveys 'yes' or 'no'. Our experimental results closely match the Turkers' response data, demonstrating that meanings can be learned from Web data and that such meanings can drive pragmatic inference.", |
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| "section": "Abstract", |
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| "text": "An important challenge for natural language processing is how to learn not only basic linguistic meanings but also how those meanings are systematically enriched when expressed in context. For instance, answers to polar (yes/no) questions do not always explicitly contain a 'yes' or 'no', but rather give information that the hearer can use to infer such an answer in a context with some degree of certainty. Hockey et al. (1997) find that 27% of answers to polar questions do not contain a direct 'yes' or 'no' word, 44% of which they regard as failing to convey a clear 'yes' or 'no' response. In some cases, interpreting the answer is straightforward (Was it bad? It was terrible.), but in others, what to infer from the answer is unclear (Was it good? It was provocative.). It is even common for the speaker to explicitly convey his own uncertainty with such answers.", |
| "cite_spans": [ |
| { |
| "start": 409, |
| "end": 429, |
| "text": "Hockey et al. (1997)", |
| "ref_id": "BIBREF10" |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "In this paper, we focus on the interpretation of answers to a particular class of polar questions: ones in which the main predication involves a gradable modifier (e.g., highly unusual, not good, little) and the answer either involves another gradable modifier or a numerical expression (e.g., seven years old, twenty acres of land). Interpreting such question-answer pairs requires dealing with modifier meanings, specifically, learning context-dependent scales of expressions (Horn, 1972; Fauconnier, 1975 ) that determine how, and to what extent, the answer as a whole resolves the issue raised by the question.", |
| "cite_spans": [ |
| { |
| "start": 478, |
| "end": 490, |
| "text": "(Horn, 1972;", |
| "ref_id": "BIBREF11" |
| }, |
| { |
| "start": 491, |
| "end": 507, |
| "text": "Fauconnier, 1975", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "We propose two methods for learning the knowledge necessary for interpreting indirect answers to questions involving gradable adjectives, depending on the type of predications in the question and the answer. The first technique deals with pairs of modifiers: we hypothesized that online, informal review corpora in which people's comments have associated ratings would provide a general-purpose database for mining scales between modifiers. We thus use a large collection of online reviews to learn orderings between adjectives based on contextual entailment (good < excellent), and employ this scalar relationship to infer a yes/no answer (subject to negation and other qualifiers). The second strategy targets numerical answers. Since it is unclear what kind of corpora would contain the relevant information, we turn to the Web in general: we use distributional information retrieved via Web searches to assess whether the numerical measure counts as a posi-tive or negative instance of the adjective in question. Both techniques exploit the same approach: using texts (the Web) to learn meanings that can drive pragmatic inference in dialogue. This paper demonstrates to some extent that meaning can be grounded from text in this way.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Indirect speech acts are studied by Clark (1979) , Perrault and , and Asher and Lascarides (2003) , who identify a wide range of factors that govern how speakers convey their intended messages and how hearers seek to uncover those messages from uncertain and conflicting signals. In the computational literature, Carberry (1994, 1999) provide an extensive model that interprets and generates indirect answers to polar questions. They propose a logical inference model which makes use of discourse plans and coherence relations to infer categorical answers. However, to adequately interpret indirect answers, the uncertainty inherent in some answers needs to be captured (de Marneffe et al., 2009) . While a straightforward 'yes' or 'no' response is clear in some indirect answers, such as in 1, the intended answer is less certain in other cases (2): 1 (1) A: Do you think that's a good idea, that we just begin to ignore these numbers? B: I think it's an excellent idea.", |
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| "start": 36, |
| "end": 48, |
| "text": "Clark (1979)", |
| "ref_id": "BIBREF4" |
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| { |
| "start": 70, |
| "end": 97, |
| "text": "Asher and Lascarides (2003)", |
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| { |
| "start": 313, |
| "end": 334, |
| "text": "Carberry (1994, 1999)", |
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| "end": 696, |
| "text": "(de Marneffe et al., 2009)", |
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| "section": "Related work", |
| "sec_num": "2" |
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| "text": "(2) A: Is he qualified? B: I think he's young.", |
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| "text": "In 2, it might be that the answerer does not know about qualifications or does not want to talk about these directly, and therefore shifts the topic slightly. As proposed by Zeevat (1994) in his work on partial answers, the speaker's indirect answer might indicate that he is deliberately leaving the original question only partially addressed, while giving a fully resolving answer to another one. The hearer must then interpret the answer to work out the other question. In (2) in context, we get a sense that the speaker would resolve the issue to 'no', but that he is definitely not committed to that in any strong sense. Uncertainty can thus reside both on the speaker and the hearer sides, and the four following scenarios are attested in conversation: a. The speaker is certain of 'yes' or 'no' and conveys that directly and successfully to the hearer. b. The speaker is certain of 'yes' or 'no' but conveys this only partially to the hearer. c. The speaker is uncertain of 'yes' or 'no' and conveys this uncertainty to the hearer. d. The speaker is uncertain of 'yes ' or 'no', but the hearer infers one of those with confidence.", |
| "cite_spans": [ |
| { |
| "start": 174, |
| "end": 187, |
| "text": "Zeevat (1994)", |
| "ref_id": "BIBREF25" |
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| "start": 1075, |
| "end": 1085, |
| "text": "' or 'no',", |
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| "sec_num": "2" |
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| "text": "The uncertainty is especially pressing for predications built around scalar modifiers, which are inherently vague and highly context-dependent (Kamp and Partee, 1995; Kennedy and McNally, 2005; Kennedy, 2007) . For example, even if we fix the basic sense for little to mean 'young for a human', there is a substantial amount of gray area between the clear instances (babies) and the clear non-instances (adults). This is the source of uncertainty in (3), in which B's children fall into the gray area.", |
| "cite_spans": [ |
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| "start": 143, |
| "end": 166, |
| "text": "(Kamp and Partee, 1995;", |
| "ref_id": "BIBREF14" |
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| { |
| "start": 167, |
| "end": 193, |
| "text": "Kennedy and McNally, 2005;", |
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| "start": 194, |
| "end": 208, |
| "text": "Kennedy, 2007)", |
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| "sec_num": "2" |
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| "text": "(3) A: Are your kids little? B: I have a seven year-old and a ten year-old.", |
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| "sec_num": "2" |
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| "text": "Since indirect answers are likely to arise in interviews, to gather instances of question-answer pairs involving gradable modifiers (which will serve to evaluate the learning techniques), we use online CNN interview transcripts from five different shows aired between 2000 and 2008 (Anderson Cooper, Larry King Live, Late Edition, Lou Dobbs Tonight, The Situation Room). We also searched the Switchboard Dialog Act corpus (Jurafsky et al., 1997) . We used regular expressions and manual filtering to find examples of twoutterance dialogues in which the question and the reply contain some kind of gradable modifier.", |
| "cite_spans": [ |
| { |
| "start": 422, |
| "end": 445, |
| "text": "(Jurafsky et al., 1997)", |
| "ref_id": "BIBREF13" |
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| ], |
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| "section": "Corpus description", |
| "sec_num": "3" |
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| "text": "In total, we ended up with 224 question-answer pairs involving gradable adjectives. However our collection contains different types of answers, which naturally fall into two categories: (I) in 205 dialogues, both the question and the answer contain a gradable modifier; (II) in 19 dialogues, the reply contains a numerical measure (as in (3) above and (4)). Category I, which consists of pairs of modifiers, can be further divided. In most dialogues, the answer contains another adjective than the one used in the question, such as in (1). In others, the answer contains the same adjective as in the question, but modified by an adverb (e.g., very, basically, quite) as in (5) or a negation as in (6).", |
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| "section": "Types of question-answer pairs", |
| "sec_num": "3.1" |
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| "text": "(5) A: That seems to be the biggest sign of progress there. Is that accurate? B: That's absolutely accurate. The negation can be present in the main clause when the adjectival predication is embedded, as in example (7).", |
| "cite_spans": [], |
| "ref_spans": [], |
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| "section": "Types of question-answer pairs", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "(7) A: [. . . ] Is that fair? B: I don't think that's a fair statement.", |
| "cite_spans": [], |
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| "section": "Types of question-answer pairs", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "In a few cases, when the question contains an adjective modifying a noun, the adjective is omitted in the answer:", |
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| "section": "Types of question-answer pairs", |
| "sec_num": "3.1" |
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| "text": "(8) A: Is that a huge gap in the system? B: It is a gap. Table 1 gives the distribution of the types appearing in the corpus.", |
| "cite_spans": [], |
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| "start": 57, |
| "end": 64, |
| "text": "Table 1", |
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| ], |
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| "section": "Types of question-answer pairs", |
| "sec_num": "3.1" |
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| "text": "To assess the degree to which each answer conveys 'yes' or 'no' in context, we use response distributions from Mechanical Turk workers. Given a written dialogue between speakers A and B, Turkers were asked to judge what B's answer conveys: 'definite yes', 'probable yes', 'uncertain', 'probable no', 'definite no'. Within each of the two 'yes' and 'no' pairs, there is a scalar relationship, but the pairs themselves are not in a scalar relationship with each other, and 'uncertain' is arguably a separate judgment. Figure 1 shows the exact formulation used in the experiment. For each dialogue, we got answers from 30 Turkers, and we took the dominant response as the correct one though we make extensive use of the full response distributions in evaluating our approach. 2 We also computed entropy values for the distribution of answers for each item. Overall, the agreement was good: 21 items have total agreement (entropy of 0.0 -11 in the \"adjective\" category, 9 in the \"adverb-adjective\" category and 1 in the \"negation\" category), and 80 items are such that a single response got chosen 20 or more times (entropy < 0.9). The dialogues in (1) and 9are examples of total agreement. In contrast, (10) has response entropy of 1.1, and item (11) has the highest entropy of 2.2. (9) A: Advertisements can be good or bad.", |
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| "text": "Figure 1", |
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| "section": "Answer assignment", |
| "sec_num": "3.2" |
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| { |
| "text": "Was it a good ad? B: It was a great ad.", |
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| "section": "Answer assignment", |
| "sec_num": "3.2" |
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| "text": "(10) A: Am I clear? B: I wish you were a little more forthright.", |
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| "section": "Answer assignment", |
| "sec_num": "3.2" |
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| "text": "(11) A: 91 percent of the American people still express confidence in the long-term prospect of the U.S. economy; only 8 percent are not confident. Are they overly optimistic, in your professional assessment?", |
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| "section": "Answer assignment", |
| "sec_num": "3.2" |
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| { |
| "text": "Indirect Answers to Yes/No Questions In the following dialogue, speaker A asks a simple Yes/No question, but speaker B answers with something more indirect and complicated.", |
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| "section": "Answer assignment", |
| "sec_num": "3.2" |
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| { |
| "text": "Which of the following best captures what speaker B meant here:", |
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| "section": "dialogue here", |
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| "text": "\u2022 B definitely meant to convey \"Yes\".", |
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| "section": "dialogue here", |
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| "text": "\u2022 B probably meant to convey \"Yes\".", |
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| "section": "dialogue here", |
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| "text": "\u2022 B definitely meant to convey \"No\".", |
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| "text": "\u2022 B probably meant to convey \"No\".", |
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| "text": "\u2022 (I really can't tell whether B meant to convey \"Yes\" or \"No\".) B: I think it shows how wise the American people are. Table 2 shows the mean entropy values for the different categories identified in the corpus. Interestingly, the pairs involving an adverbial modification in the answer all received a positive answer ('yes' or 'probable yes') as dominant response. All 19 dialogues involving a numerical measure had either 'probable yes' or 'uncertain' as dominant response. There is thus a significant bias for positive answers: 70% of the category I items and 74% of the category II items have a positive answer as dominant response. Examining a subset of the Dialog Act corpus, we found that 38% of the yes/no questions receive a direct positive answers, whereas 21% have a direct negative answer. This bias probably stems from the fact that people are more likely to use an overt denial expression where they need to disagree, whether or not they are responding indirectly.", |
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| "start": 119, |
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| "text": "Table 2", |
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| "text": "In this section, we present the methods we propose for grounding the meanings of scalar modifiers.", |
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| "text": "The first technique targets items such as the ones in category I of our corpus. Our central hypothesis is that, in polar question dialogues, the semantic relationship between the main predication P Q in the question and the main predication P A in the answer is the primary factor in determining whether, and to what extent, 'yes' or 'no' was intended. If P A is at least as strong as P Q , the intended answer is 'yes'; if P A is weaker than P Q , the intended answer is 'no'; and, where no reliable entailment relationship exists between P A and P Q , the result is uncertainty. For example, good is weaker (lower on the relevant scale) than excellent, and thus speakers infer that the reply in example (1) above is meant to convey 'yes'. In contrast, if we reverse the order of the modifiers -roughly, Is it a great idea?; It's a good idea -then speakers infer that the answer conveys 'no'. Had B replied with It's a complicated idea in (1), then uncertainty would likely have resulted, since good and complicated are not in a reliable scalar relationship. Negation reverses scales (Horn, 1972; Levinson, 2000) , so it flips 'yes' and 'no' in these cases, leaving 'uncertain' unchanged. When both the question and the answer contain a modifier (such as in (9-11)), the yes/no response should correlate with the extent to which the pair of modifiers can be put into a scale based on contextual entailment.", |
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| "text": "(Horn, 1972;", |
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| "text": "Levinson, 2000)", |
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| "section": "Learning modifier scales and inferring yes/no answers", |
| "sec_num": "4.1" |
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| "text": "To ground such scales from text, we collected a large corpus of online reviews from IMDB. Each of the reviews in this collection has an associated star rating: one star (most negative) to ten stars (most positive). Table 3 summarizes the distribution of reviews as well as the number of words and vocabulary across the ten rating categories.", |
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| "text": "Table 3", |
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| "text": "As is evident from table 3, there is a significant bias for ten-star reviews. This is a common feature of such corpora of informal, userprovided reviews (Chevalier and Mayzlin, 2006; Hu et al., 2006; Pang and Lee, 2008) . However, since we do not want to incorporate the linguistically uninteresting fact that people tend to write a lot of ten-star reviews, we assume uniform priors for the rating categories. Let count(w, r) be the number of tokens of word w in reviews in rating category r, and let count(r) be the total word count for all words in rating category r. The probability of w given a rating category r is simply Pr(w|r) = count(w, r)/ count(r). Then under the assumption of uniform priors, we get Pr(r|w) = Pr(w|r)/ r \u2208R Pr(w|r ).", |
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| "start": 153, |
| "end": 182, |
| "text": "(Chevalier and Mayzlin, 2006;", |
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| "text": "Hu et al., 2006;", |
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| "text": "Pang and Lee, 2008)", |
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| "section": "Learning modifier scales and inferring yes/no answers", |
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| "text": "In reasoning about our dialogues, we rescale the rating categories by subtracting 5.5 from each, to center them at 0. This yields the scale R =", |
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| "section": "Learning modifier scales and inferring yes/no answers", |
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| "text": "Words Vocabulary Average words per review 1 124,587 25,389,211 192,348 203.79 2 51,390 11,750,820 133,283 228.66 3 58,051 13,990,519 148,530 241.00 4 59,781 14,958,477 156,564 250.22 5 80,487 20,382,805 188,461 253.24 6 106,145 27,408,662 225,165 258.22 7 157, Pr(Rating|Word)", |
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| "text": "Vocabulary Average words per review 1 124,587 25,389,211 192,348 203.79 2 51,390 11,750,820 133,283 228.66 3 58,051 13,990,519 148,530 241.00 4 59,781 14,958,477 156,564 250.22 5 80,487 20,382,805 188,461 253.24 6 106,145 27,408,662 225,165 258.22 7 157,", |
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| } |
| ], |
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| "section": "Reviews", |
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| "text": "Rating (centered at 0) Figure 2 : The distribution of some scalar modifiers across the ten rating categories. The vertical lines mark the expected ratings, defined as a weighted sum of the probability values (black dots).", |
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| "text": "Figure 2", |
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| "text": "\u22124.5, \u22123.5, \u22122.5, \u22121.5, \u22120.5, 0.5, 1.5, 2.5, 3.5, 4.5 . Our rationale for this is that modifiers at the negative end of the scale (bad, awful, terrible) are not linguistically comparable to those at the positive end of the scale (good, excellent, superb). Each group forms its own qualitatively different scale (Kennedy and McNally, 2005) . Rescaling allows us to make a basic positive vs. negative distinction. Once we have done that, an increase in absolute value is an increase in strength. In our experiments, we use expected rating values to characterize the polarity and strength of modifiers. The expected rating value for a word w is ER(w) = r\u2208R r Pr(r|w). Figure plots these values for a number of scalar terms, both positive and negative, across the rescaled ratings, with the vertical lines marking their ER values. The weak scalar modifiers all the way on the left are most common near the middle of the scale, with a slight positive bias in the top row and a slight negative bias in the bottom row. As we move from left to right, the bias for one end of the scale grows more extreme, until the words in question are almost never used outside of the most extreme rating category. The resulting scales correspond well with linguistic intuitions and thus provide an initial indication that the rating categories are a reliable guide to strength and polarity for scalar modifiers. We put this information to use in our dialogue corpus via the decision procedure Let D be a dialogue consisting of (i) a polar question whose main predication is based on scalar predicate P Q and (ii) an indirect answer whose main predication is based on scalar predicate P A . Then:", |
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| "start": 311, |
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| "text": "(Kennedy and McNally, 2005)", |
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| "text": "1. if P A or P Q is missing from our data, infer 'Uncertain';", |
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| "text": "2. else if ER(P Q ) and ER(P A ) have different signs, infer 'No';", |
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| "eq_spans": [], |
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| "text": "3. else if abs(ER(P Q )) abs(ER(P A )), infer 'Yes';", |
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| "eq_spans": [], |
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| "text": "4. else infer 'No'.", |
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| "eq_spans": [], |
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| "sec_num": null |
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| "text": "5. In the presence of negation, map 'Yes' to 'No', 'No' to 'Yes', and 'Uncertain' to 'Uncertain'. ", |
| "cite_spans": [ |
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| "start": 36, |
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| "text": "'Yes' to 'No', 'No'", |
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| "text": "The second technique aims at determining whether a numerical answer counts as a positive or negative instance of the adjective in the question (category II in our corpus). Adjectives that can receive a conventional unit of measure, such as little or long, inherently possess a degree of vagueness (Kamp and Partee, 1995; Kennedy, 2007) : while in the extreme cases, judgments are strong (e.g., a six foot tall woman can clearly be called \"a tall woman\" whereas a five foot tall woman cannot), there are borderline cases for which it is difficult to say whether the adjectival predication can truthfully be ascribed to them. A logistic regression model can capture these facts. To build this model, we gather distributional information from the Web.", |
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| "start": 297, |
| "end": 320, |
| "text": "(Kamp and Partee, 1995;", |
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| "end": 335, |
| "text": "Kennedy, 2007)", |
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| "section": "Interpreting numerical answers", |
| "sec_num": "4.2" |
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| "text": "For instance, in the case of (3), we can retrieve from the Web positive and negative examples of age in relation to the adjective and the modified entity \"little kids\". The question contains the adjective and the modified entity. The reply contains the unit of measure (here \"year-old\") and the numerical answer. Specifically we query the Web using Yahoo! BOSS (Academic) for \"little kids\" yearold (positive instances) as well as for \"not little kids\" year-old (negative instances). Yahoo! BOSS is an open search services platform that provides a query API for Yahoo! Web search. We then ex-tract ages from the positive and negative snippets obtained, and we fit a logistic regression to these data. To remove noise, we discard low counts (positive and negative instances for a given unit < 5). Also, for some adjectives, such as little or young, there is an inherent ambiguity between absolute and relative uses. Ideally, a word sense disambiguation system would be used to filter these cases. For now, we extract the largest contiguous range for which the data counts are over the noise threshold. 3 When not enough data is retrieved for the negative examples, we expand the query by moving the negation outside the search phrase. We also replace the negation and the adjective by the antonyms given in WordNet (using the first sense).", |
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| "text": "The logistic regression thus has only one factor -the unit of measure (age in the case of little kids). For a given answer, the model assigns a probability indicating the extent to which the adjectival property applies to that answer. If the factor is a significant predictor, we can use the probabilities from the model to decide whether the answer qualifies as a positive or negative instance of the adjective in the question, and thus interpret the indirect response as a 'yes' or a 'no'. The probabilistic nature of this technique adheres perfectly to the fact that indirect answers are intimately tied up with uncertainty.", |
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| "text": "Our primary goal is to evaluate how well we can learn the relevant scalar and entailment relationships from the Web. In the evaluation, we thus applied our techniques to a manually coded corpus version. For the adjectival scales, we annotated each example for its main predication (modifier, or adverb-modifier bigram), including whether that predication was negated. For the numerical cases, we manually constructed the initial queries: we identified the adjective and the modified entity in the question, and the unit of measure in the answer. However, we believe that identifying the requisite predications and recognizing the presence of negation or embedding could be done automatically using dependency graphs. 4 I Other adjective 60 60 Adverb -same adjective 95 95 Negation -same adjective 100 100 Omitted adjective 100 100 II Numerical 89 40 Total 75 71 Table 5 : Precision, recall, and F1 (%) per response category. In the case of the scalar modifiers experiment, there were just two examples whose dominant response from the Turkers was 'Uncertain', so we have left that category out of the results. In the case of the numerical experiment, there were not any 'No' answers.", |
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| "text": "I Other adjective 60 60 Adverb -same adjective 95 95 Negation -same adjective 100 100 Omitted adjective 100 100 II Numerical 89 40 Total 75 71", |
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| }, |
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| "end": 888, |
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| "section": "Evaluation and results", |
| "sec_num": "5" |
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| "text": "To evaluate the techniques, we pool the Mechanical Turk 'definite yes' and 'probable yes' categories into a single category 'Yes', and we do the same for 'definite no' and 'probable no'. Together with 'uncertain', this makes for threeresponse categories. We count an inference as successful if it matches the dominant Turker response category. To use the three-response scheme in the numerical experiment, we simply categorize the probabilities as follows: 0-0.33 = 'No', 0.33-0.66 = 'Uncertain', 0.66-1.00 = 'Yes'. Table 4 gives a breakdown of our system's performance on the various category subtypes. The overall accuracy level is 71% (159 out of 224 inferences correct). Table 5 summarizes the results per response category, for the examples in which both the question and answer contain a gradable modifier (category I), and for the numerical cases (category II).", |
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| "section": "Modification in answer Precision Recall", |
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| "text": "Performance is extremely good on the \"Adverbsame adjective\" and \"Negation -same adjective\" cases because the 'Yes' answer is fairly direct for them (though adverbs like basically introduce an interesting level of uncertainty). The results are because of a parse error which led to wrong dependencies. Table 6 : Precision, recall, and F1 (%) per response category for the WordNet-based approach.", |
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| "start": 301, |
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| "section": "Analysis and discussion", |
| "sec_num": "6" |
| }, |
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| "text": "somewhat mixed for the \"Other adjective\" category.", |
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| "section": "Analysis and discussion", |
| "sec_num": "6" |
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| "text": "Inferring the relation between scalar adjectives has some connection with work in sentiment detection. Even though most of the research in that domain focuses on the orientation of one term using seed sets, techniques which provide the orientation strength could be used to infer a scalar relation between adjectives. For instance, Blair-Goldensohn et al. (2008) use WordNet to develop sentiment lexicons in which each word has a positive or negative value associated with it, representing its strength. The algorithm begins with seed sets of positive, negative, and neutral terms, and then uses the synonym and antonym structure of WordNet to expand those initial sets and refine the relative strength values. Using our own seed sets, we built a lexicon using Blair-Goldensohn et al. (2008) 's method and applied it as in figure 3 (changing the ER values to sentiment scores). Both approaches achieve similar results: for the \"Other adjective\" category, the WordNet-based approach yields 56% accuracy, which is not significantly different from our performance (60%); for the other types in category I, there is no difference in results between the two methods. Table 6 summarizes the results per response category for the WordNet-based approach (which can thus be compared to the category I results in table 5). However in contrast to the WordNet-based approach, we require no hand-built resources: the synonym and antonym structures, as well as the strength values, are learned from Web data alone. In addition, the WordNet-based approach must be supplemented with a separate method for the numerical cases.", |
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| "text": "In the \"Other adjective\" category, 31 items involve oppositional terms: canonical antonyms (e.g., right/wrong, good/bad) as well as terms that are \"statistically oppositional\" (e.g., ready/ premature, true/preposterous, confident/nervous). \"Statistically oppositional\" terms are not oppositional by definition, but as a matter of contingent fact. Our technique accurately deals with most ", |
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| "text": "Degree Probability of being \"warm\" Figure 4 : Probabilities of being appropriately described as \"little\", \"young\" or \"warm\", fitted on data retrieved when querying the Web for \"little kids\", \"young kids\" and \"warm weather\".", |
| "cite_spans": [], |
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| "start": 35, |
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| "text": "of the canonical antonyms, and also finds some contingent oppositions (qualified/young, wise/ neurotic) that are lacking in antonymy resources or automatically generated antonymy lists (Mohammad et al., 2008) . Out of these 31 items, our technique correctly marks 18, whereas Mohammad et al.'s list of antonyms only contains 5 and Blair-Goldensohn et al. (2008) 's technique finds 11. Our technique is solely based on unigrams, and could be improved by adding context: making use of dependency information, as well as moving beyond unigrams. In the numerical cases, precision is high but recall is low. For roughly half of the items, not enough negative instances can be gathered from the Web and the model lacks predictive power (as for items (4) or (12)). (12) A: Do you happen to be working for a large firm? B: It's about three hundred and fifty people. Looking at the negative hits for item (12), one sees that few give an indication about the number of people in the firm, but rather qualifications about colleagues or employees (great people, people's productivity), or the hits are less relevant: \"Most of the people I talked to were actually pretty optimistic. They were rosy on the job market and many had jobs, although most were not large firm jobs\". The lack of data comes from the fact that the queries are very specific, since the adjective depends on the product (e.g., \"expensive exercise bike\", \"deep pond\"). However when we do get a predictive model, the probabilities corre- Figure 5 : Correlation between agreement among Turkers and whether the system gets the correct answer. For each dialogue, we plot a circle at Turker response entropy and either 1 = correct inference or 0 = incorrect inference, except the points are jittered a little vertically to show where the mass of data lies. As the entropy rises (i.e., as agreement levels fall), the system's inferences become less accurate. The fitted logistic regression model (black line) has a statistically significant coefficient for response entropy (p < 0.001).", |
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| "start": 185, |
| "end": 208, |
| "text": "(Mohammad et al., 2008)", |
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| }, |
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| "start": 331, |
| "end": 361, |
| "text": "Blair-Goldensohn et al. (2008)", |
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| ], |
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| { |
| "text": "late almost perfectly with the Turkers' responses. This happens for 8 items: \"expensive to call (50 cents a minute)\", \"little kids (7 and 10 year-old)\", \"long growing season (3 months)\", \"lot of land (80 acres)\", \"warm weather (80 degrees)\", \"young kids (5 and 2 year-old)\", \"young person (31 yearold)\" and \"large house (2450 square feet)\". In the latter case only, the system output (uncertain) doesn't correlate with the Turkers' judgment (where the dominant answer is 'probable yes' with 15 responses, and 11 answers are 'uncertain').", |
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| "text": "The logistic curves in figure 4 capture nicely the intuitions that people have about the relation between age and \"little kids\" or \"young kids\", as well as between Fahrenheit degrees and \"warm weather\". For \"little kids\", the probabilities of being little or not are clear-cut for ages below 7 and above 15, but there is a region of vagueness in between. In the case of \"young kids\", the probabilities drop less quickly with age increasing (an 18 year-old can indeed still be qualified as a \"young kid\"). In sum, when the data is available, this method delivers models which fit humans' intuitions about the relation between numerical measure and adjective, and can handle pragmatic inference.", |
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| "text": "If we restrict attention to the 66 examples on which the Turkers completely agreed about which of these three categories was intended (again pooling 'probable' and 'definite'), then the percentage of correct inferences rises to 89% (59 correct inferences). Figure 5 plots the relationship between the response entropy and the accuracy of our decision procedure, along with a fitted logistic regression model using response entropy to predict whether our system's inference was correct. The handful of empirical points in the lower left of the figure show cases of high agreement between Turkers but incorrect inference from the system. The few points in the upper right indicate low agreement between Turkers and correct inference from the system. Three of the high-agreement/incorrect-inference cases involve the adjectives right-correct. For lowagreement/correct-inference, the disparity could trace to context dependency: the ordering is clear in the context of product reviews, but unclear in a television interview. The analysis suggests that overall agreement is positively correlated with our system's chances of making a correct inference: our system's accuracy drops as human agreement levels drop.", |
| "cite_spans": [], |
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| "text": "We set out to find techniques for grounding basic meanings from text and enriching those meanings based on information from the immediate linguistic context. We focus on gradable modifiers, seeking to learn scalar relationships between their meanings and to obtain an empirically grounded, probabilistic understanding of the clear and fuzzy cases that they often give rise to (Kamp and Partee, 1995) . We show that it is possible to learn the requisite scales between modifiers using review corpora, and to use that knowledge to drive inference in indirect responses. When the relation in question is not too specific, we show that it is also possible to learn the strength of the relation between an adjective and a numerical measure.", |
| "cite_spans": [ |
| { |
| "start": 376, |
| "end": 399, |
| "text": "(Kamp and Partee, 1995)", |
| "ref_id": "BIBREF14" |
| } |
| ], |
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| "section": "Conclusion", |
| "sec_num": "7" |
| }, |
| { |
| "text": "Here and throughout, the examples come from the corpus described in section 3.", |
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| "text": "120 Turkers were involved (the median number of items done was 28 and the mean 56.5). The Fleiss' Kappa score for the five response categories is 0.46, though these categories are partially ordered. For the three-category response system used in section 5, which arguably has no scalar ordering, the Fleiss' Kappa is 0.63. Despite variant individual judgments, aggregate annotations done with Mechanical Turk have been shown to be reliable(Snow et al., 2008;Sheng et al., 2008;Munro et al., 2010). Here, the relatively low Kappa scores also reflect the uncertainty inherent in many of our examples, uncertainty that we seek to characterize and come to grips with computationally.", |
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| "text": "Otherwise, our model is ruined by references to \"young 80-year olds\", using the relative sense of young, which are moderately frequent on the Web.4 As a test, we transformed our corpus into the Stanford dependency representation(de Marneffe et al., 2006), using the Stanford parser(Klein and Manning, 2003) and were able to automatically retrieve all negated modifier predications, except one (We had a view of it, not a particularly good one),", |
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| { |
| "text": "This paper is based on work funded in part by ONR award N00014-10-1-0109 and ARO MURI award 548106, as well as by the Air Force Research Laboratory (AFRL) under prime contract no. FA8750-09-C-0181. Any opinions, findings, and conclusion or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the Air Force Research Laboratory (AFRL), ARO or ONR.", |
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| "section": "Acknowledgments", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
| "ref_id": "b0", |
| "title": "Analyzing intention in utterances", |
| "authors": [ |
| { |
| "first": "James", |
| "middle": [ |
| "F" |
| ], |
| "last": "Allen", |
| "suffix": "" |
| }, |
| { |
| "first": "C", |
| "middle": [ |
| "Raymond" |
| ], |
| "last": "Perrault", |
| "suffix": "" |
| } |
| ], |
| "year": 1980, |
| "venue": "Artificial Intelligence", |
| "volume": "15", |
| "issue": "", |
| "pages": "143--178", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "James F. Allen and C. Raymond Perrault. 1980. Ana- lyzing intention in utterances. Artificial Intelligence, 15:143-178.", |
| "links": null |
| }, |
| "BIBREF1": { |
| "ref_id": "b1", |
| "title": "Logics of Conversation", |
| "authors": [ |
| { |
| "first": "Nicholas", |
| "middle": [], |
| "last": "Asher", |
| "suffix": "" |
| }, |
| { |
| "first": "Alex", |
| "middle": [], |
| "last": "Lascarides", |
| "suffix": "" |
| } |
| ], |
| "year": 2003, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nicholas Asher and Alex Lascarides. 2003. Logics of Conversation. Cambridge University Press, Cam- bridge.", |
| "links": null |
| }, |
| "BIBREF2": { |
| "ref_id": "b2", |
| "title": "Building a sentiment summarizer for local service reviews", |
| "authors": [ |
| { |
| "first": "Sasha", |
| "middle": [], |
| "last": "Blair-Goldensohn", |
| "suffix": "" |
| }, |
| { |
| "first": "Kerry", |
| "middle": [], |
| "last": "Hannan", |
| "suffix": "" |
| }, |
| { |
| "first": "Ryan", |
| "middle": [], |
| "last": "Mcdonald", |
| "suffix": "" |
| }, |
| { |
| "first": "Tyler", |
| "middle": [], |
| "last": "Neylon", |
| "suffix": "" |
| }, |
| { |
| "first": "George", |
| "middle": [ |
| "A" |
| ], |
| "last": "Reis", |
| "suffix": "" |
| }, |
| { |
| "first": "Jeff", |
| "middle": [], |
| "last": "Reynar", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "WWW Workshop on NLP in the Information Explosion Era (NLPIX)", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Sasha Blair-Goldensohn, Kerry Hannan, Ryan McDon- ald, Tyler Neylon, George A. Reis, and Jeff Reynar. 2008. Building a sentiment summarizer for local service reviews. In WWW Workshop on NLP in the Information Explosion Era (NLPIX).", |
| "links": null |
| }, |
| "BIBREF3": { |
| "ref_id": "b3", |
| "title": "The effect of word of mouth on sales: Online book reviews", |
| "authors": [ |
| { |
| "first": "Judith", |
| "middle": [ |
| "A" |
| ], |
| "last": "Chevalier", |
| "suffix": "" |
| }, |
| { |
| "first": "Dina", |
| "middle": [], |
| "last": "Mayzlin", |
| "suffix": "" |
| } |
| ], |
| "year": 2006, |
| "venue": "Journal of Marketing Research", |
| "volume": "43", |
| "issue": "3", |
| "pages": "345--354", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Judith A. Chevalier and Dina Mayzlin. 2006. The effect of word of mouth on sales: Online book re- views. Journal of Marketing Research, 43(3):345- 354.", |
| "links": null |
| }, |
| "BIBREF4": { |
| "ref_id": "b4", |
| "title": "Responding to indirect speech acts", |
| "authors": [ |
| { |
| "first": "H", |
| "middle": [], |
| "last": "Herbert", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Clark", |
| "suffix": "" |
| } |
| ], |
| "year": 1979, |
| "venue": "Cognitive Psychology", |
| "volume": "11", |
| "issue": "", |
| "pages": "430--477", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Herbert H. Clark. 1979. Responding to indirect speech acts. Cognitive Psychology, 11:430-477.", |
| "links": null |
| }, |
| "BIBREF5": { |
| "ref_id": "b5", |
| "title": "Generating typed dependency parses from phrase structure parses", |
| "authors": [ |
| { |
| "first": "Marie-Catherine", |
| "middle": [], |
| "last": "De Marneffe", |
| "suffix": "" |
| }, |
| { |
| "first": "Bill", |
| "middle": [], |
| "last": "Maccartney", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
| ], |
| "last": "Manning", |
| "suffix": "" |
| } |
| ], |
| "year": 2006, |
| "venue": "Proceedings of the 5th International Conference on Language Resources and Evaluation", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marie-Catherine de Marneffe, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC-2006).", |
| "links": null |
| }, |
| "BIBREF6": { |
| "ref_id": "b6", |
| "title": "Not a simple 'yes' or 'no': Uncertainty in indirect answers", |
| "authors": [ |
| { |
| "first": "Marie-Catherine", |
| "middle": [], |
| "last": "De Marneffe", |
| "suffix": "" |
| }, |
| { |
| "first": "Scott", |
| "middle": [], |
| "last": "Grimm", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [], |
| "last": "Potts", |
| "suffix": "" |
| } |
| ], |
| "year": 2009, |
| "venue": "Proceedings of the 10th Annual SIGDIAL Meeting on Discourse and Dialogue", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Marie-Catherine de Marneffe, Scott Grimm, and Christopher Potts. 2009. Not a simple 'yes' or 'no': Uncertainty in indirect answers. In Proceed- ings of the 10th Annual SIGDIAL Meeting on Dis- course and Dialogue.", |
| "links": null |
| }, |
| "BIBREF7": { |
| "ref_id": "b7", |
| "title": "Pragmatic scales and logical structure", |
| "authors": [ |
| { |
| "first": "Gilles", |
| "middle": [], |
| "last": "Fauconnier", |
| "suffix": "" |
| } |
| ], |
| "year": 1975, |
| "venue": "Linguistic Inquiry", |
| "volume": "6", |
| "issue": "3", |
| "pages": "353--375", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Gilles Fauconnier. 1975. Pragmatic scales and logical structure. Linguistic Inquiry, 6(3):353-375.", |
| "links": null |
| }, |
| "BIBREF8": { |
| "ref_id": "b8", |
| "title": "A hybrid reasoning model for indirect answers", |
| "authors": [ |
| { |
| "first": "Nancy", |
| "middle": [], |
| "last": "Green", |
| "suffix": "" |
| }, |
| { |
| "first": "Sandra", |
| "middle": [], |
| "last": "Carberry", |
| "suffix": "" |
| } |
| ], |
| "year": 1994, |
| "venue": "Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "58--65", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nancy Green and Sandra Carberry. 1994. A hybrid reasoning model for indirect answers. In Proceed- ings of the 32nd Annual Meeting of the Association for Computational Linguistics, pages 58-65.", |
| "links": null |
| }, |
| "BIBREF9": { |
| "ref_id": "b9", |
| "title": "Interpreting and generating indirect answers", |
| "authors": [ |
| { |
| "first": "Nancy", |
| "middle": [], |
| "last": "Green", |
| "suffix": "" |
| }, |
| { |
| "first": "Sandra", |
| "middle": [], |
| "last": "Carberry", |
| "suffix": "" |
| } |
| ], |
| "year": 1999, |
| "venue": "Computational Linguistics", |
| "volume": "25", |
| "issue": "3", |
| "pages": "389--435", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nancy Green and Sandra Carberry. 1999. Interpret- ing and generating indirect answers. Computational Linguistics, 25(3):389-435.", |
| "links": null |
| }, |
| "BIBREF10": { |
| "ref_id": "b10", |
| "title": "Can you predict answers to Y/N questions? Yes, No and Stuff", |
| "authors": [ |
| { |
| "first": "Deborah", |
| "middle": [], |
| "last": "Beth Ann Hockey", |
| "suffix": "" |
| }, |
| { |
| "first": "Beverly", |
| "middle": [], |
| "last": "Rossen-Knill", |
| "suffix": "" |
| }, |
| { |
| "first": "Matthew", |
| "middle": [], |
| "last": "Spejewski", |
| "suffix": "" |
| }, |
| { |
| "first": "Stephen", |
| "middle": [], |
| "last": "Stone", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Isard", |
| "suffix": "" |
| } |
| ], |
| "year": 1997, |
| "venue": "Proceedings of Eurospeech", |
| "volume": "", |
| "issue": "", |
| "pages": "2267--2270", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Beth Ann Hockey, Deborah Rossen-Knill, Beverly Spejewski, Matthew Stone, and Stephen Isard. 1997. Can you predict answers to Y/N questions? Yes, No and Stuff. In Proceedings of Eurospeech 1997, pages 2267-2270.", |
| "links": null |
| }, |
| "BIBREF11": { |
| "ref_id": "b11", |
| "title": "On the Semantic Properties of Logical Operators in English", |
| "authors": [ |
| { |
| "first": "", |
| "middle": [], |
| "last": "Laurence R Horn", |
| "suffix": "" |
| } |
| ], |
| "year": 1972, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Laurence R Horn. 1972. On the Semantic Properties of Logical Operators in English. Ph.D. thesis, UCLA, Los Angeles.", |
| "links": null |
| }, |
| "BIBREF12": { |
| "ref_id": "b12", |
| "title": "Can online reviews reveal a product's true quality?: Empirical findings and analytical modeling of online word-of-mouth communication", |
| "authors": [ |
| { |
| "first": "Nan", |
| "middle": [], |
| "last": "Hu", |
| "suffix": "" |
| }, |
| { |
| "first": "Paul", |
| "middle": [ |
| "A" |
| ], |
| "last": "Pavlou", |
| "suffix": "" |
| }, |
| { |
| "first": "Jennifer", |
| "middle": [], |
| "last": "Zhang", |
| "suffix": "" |
| } |
| ], |
| "year": 2006, |
| "venue": "Proceedings of Electronic Commerce (EC)", |
| "volume": "", |
| "issue": "", |
| "pages": "324--330", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Nan Hu, Paul A. Pavlou, and Jennifer Zhang. 2006. Can online reviews reveal a product's true quality?: Empirical findings and analytical modeling of online word-of-mouth communication. In Proceedings of Electronic Commerce (EC), pages 324-330.", |
| "links": null |
| }, |
| "BIBREF13": { |
| "ref_id": "b13", |
| "title": "Switchboard SWBD-DAMSL shallowdiscourse-function annotation coders manual, draft 13", |
| "authors": [ |
| { |
| "first": "Daniel", |
| "middle": [], |
| "last": "Jurafsky", |
| "suffix": "" |
| }, |
| { |
| "first": "Elizabeth", |
| "middle": [], |
| "last": "Shriberg", |
| "suffix": "" |
| }, |
| { |
| "first": "Debra", |
| "middle": [], |
| "last": "Biasca", |
| "suffix": "" |
| } |
| ], |
| "year": 1997, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Daniel Jurafsky, Elizabeth Shriberg, and Debra Bi- asca. 1997. Switchboard SWBD-DAMSL shallow- discourse-function annotation coders manual, draft 13. Technical Report 97-02, University of Colorado, Boulder Institute of Cognitive Science.", |
| "links": null |
| }, |
| "BIBREF14": { |
| "ref_id": "b14", |
| "title": "Prototype theory and compositionality", |
| "authors": [ |
| { |
| "first": "Hans", |
| "middle": [], |
| "last": "Kamp", |
| "suffix": "" |
| }, |
| { |
| "first": "Barbara", |
| "middle": [ |
| "H" |
| ], |
| "last": "Partee", |
| "suffix": "" |
| } |
| ], |
| "year": 1995, |
| "venue": "Cognition", |
| "volume": "57", |
| "issue": "2", |
| "pages": "129--191", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Hans Kamp and Barbara H. Partee. 1995. Prototype theory and compositionality. Cognition, 57(2):129- 191.", |
| "links": null |
| }, |
| "BIBREF15": { |
| "ref_id": "b15", |
| "title": "Scale structure and the semantic typology of gradable predicates", |
| "authors": [ |
| { |
| "first": "Christopher", |
| "middle": [], |
| "last": "Kennedy", |
| "suffix": "" |
| }, |
| { |
| "first": "Louise", |
| "middle": [], |
| "last": "Mcnally", |
| "suffix": "" |
| } |
| ], |
| "year": 2005, |
| "venue": "Language", |
| "volume": "81", |
| "issue": "2", |
| "pages": "345--381", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Christopher Kennedy and Louise McNally. 2005. Scale structure and the semantic typology of grad- able predicates. Language, 81(2):345-381.", |
| "links": null |
| }, |
| "BIBREF16": { |
| "ref_id": "b16", |
| "title": "Vagueness and grammar: The semantics of relative and absolute gradable adjectives", |
| "authors": [ |
| { |
| "first": "Christopher", |
| "middle": [], |
| "last": "Kennedy", |
| "suffix": "" |
| } |
| ], |
| "year": 2007, |
| "venue": "Linguistics and Philosophy", |
| "volume": "30", |
| "issue": "1", |
| "pages": "1--45", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Christopher Kennedy. 2007. Vagueness and grammar: The semantics of relative and absolute gradable ad- jectives. Linguistics and Philosophy, 30(1):1-45.", |
| "links": null |
| }, |
| "BIBREF17": { |
| "ref_id": "b17", |
| "title": "Accurate unlexicalized parsing", |
| "authors": [ |
| { |
| "first": "Dan", |
| "middle": [], |
| "last": "Klein", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [ |
| "D" |
| ], |
| "last": "Manning", |
| "suffix": "" |
| } |
| ], |
| "year": 2003, |
| "venue": "Proceedings of the 41st Meeting of the Association of Computational Linguistics", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Dan Klein and Christopher D. Manning. 2003. Ac- curate unlexicalized parsing. In Proceedings of the 41st Meeting of the Association of Computational Linguistics.", |
| "links": null |
| }, |
| "BIBREF18": { |
| "ref_id": "b18", |
| "title": "Presumptive Meanings: The Theory of Generalized Conversational Implicature", |
| "authors": [ |
| { |
| "first": "C", |
| "middle": [], |
| "last": "Stephen", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Levinson", |
| "suffix": "" |
| } |
| ], |
| "year": 2000, |
| "venue": "", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Stephen C. Levinson. 2000. Presumptive Meanings: The Theory of Generalized Conversational Implica- ture. MIT Press, Cambridge, MA.", |
| "links": null |
| }, |
| "BIBREF19": { |
| "ref_id": "b19", |
| "title": "Computing word-pair antonymy", |
| "authors": [ |
| { |
| "first": "Saif", |
| "middle": [], |
| "last": "Mohammad", |
| "suffix": "" |
| }, |
| { |
| "first": "Bonnie", |
| "middle": [], |
| "last": "Dorr", |
| "suffix": "" |
| }, |
| { |
| "first": "Graeme", |
| "middle": [], |
| "last": "Hirst", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Saif Mohammad, Bonnie Dorr, and Graeme Hirst. 2008. Computing word-pair antonymy. In Proceed- ings of the Conference on Empirical Methods in Nat- ural Language Processing and Computational Nat- ural Language Learning (EMNLP-2008).", |
| "links": null |
| }, |
| "BIBREF20": { |
| "ref_id": "b20", |
| "title": "Crowdsourcing and language studies: The new generation of linguistic data", |
| "authors": [ |
| { |
| "first": "Robert", |
| "middle": [], |
| "last": "Munro", |
| "suffix": "" |
| }, |
| { |
| "first": "Steven", |
| "middle": [], |
| "last": "Bethard", |
| "suffix": "" |
| }, |
| { |
| "first": "Victor", |
| "middle": [], |
| "last": "Kuperman", |
| "suffix": "" |
| }, |
| { |
| "first": "Vicky", |
| "middle": [ |
| "Tzuyin" |
| ], |
| "last": "Lai", |
| "suffix": "" |
| }, |
| { |
| "first": "Robin", |
| "middle": [], |
| "last": "Melnick", |
| "suffix": "" |
| }, |
| { |
| "first": "Christopher", |
| "middle": [], |
| "last": "Potts", |
| "suffix": "" |
| }, |
| { |
| "first": "Tyler", |
| "middle": [], |
| "last": "Schnoebelen", |
| "suffix": "" |
| }, |
| { |
| "first": "Harry", |
| "middle": [], |
| "last": "Tily", |
| "suffix": "" |
| } |
| ], |
| "year": 2010, |
| "venue": "NAACL 2010 Workshop on Creating Speech and Language Data With Amazon's Mechanical Turk", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Robert Munro, Steven Bethard, Victor Kuperman, Vicky Tzuyin Lai, Robin Melnick, Christopher Potts, Tyler Schnoebelen, and Harry Tily. 2010. Crowdsourcing and language studies: The new gen- eration of linguistic data. In NAACL 2010 Workshop on Creating Speech and Language Data With Ama- zon's Mechanical Turk.", |
| "links": null |
| }, |
| "BIBREF21": { |
| "ref_id": "b21", |
| "title": "Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval", |
| "authors": [ |
| { |
| "first": "Bo", |
| "middle": [], |
| "last": "Pang", |
| "suffix": "" |
| }, |
| { |
| "first": "Lillian", |
| "middle": [], |
| "last": "Lee", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "", |
| "volume": "2", |
| "issue": "", |
| "pages": "1--135", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in In- formation Retrieval, 2(1):1-135.", |
| "links": null |
| }, |
| "BIBREF22": { |
| "ref_id": "b22", |
| "title": "A plan-based analysis of indirect speech acts", |
| "authors": [ |
| { |
| "first": "C", |
| "middle": [], |
| "last": "", |
| "suffix": "" |
| }, |
| { |
| "first": "Raymond", |
| "middle": [], |
| "last": "Perrault", |
| "suffix": "" |
| }, |
| { |
| "first": "James", |
| "middle": [ |
| "F" |
| ], |
| "last": "Allen", |
| "suffix": "" |
| } |
| ], |
| "year": 1980, |
| "venue": "American Journal of Computational Linguistics", |
| "volume": "6", |
| "issue": "3-4", |
| "pages": "167--182", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "C. Raymond Perrault and James F. Allen. 1980. A plan-based analysis of indirect speech acts. Amer- ican Journal of Computational Linguistics, 6(3- 4):167-182.", |
| "links": null |
| }, |
| "BIBREF23": { |
| "ref_id": "b23", |
| "title": "Get another label? improving data quality and data mining using multiple, noisy labelers", |
| "authors": [ |
| { |
| "first": "S", |
| "middle": [], |
| "last": "Victor", |
| "suffix": "" |
| }, |
| { |
| "first": "Foster", |
| "middle": [], |
| "last": "Sheng", |
| "suffix": "" |
| }, |
| { |
| "first": "Panagiotis", |
| "middle": [ |
| "G" |
| ], |
| "last": "Provost", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Ipeirotis", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proceedings of KDD", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Victor S. Sheng, Foster Provost, and Panagiotis G. Ipeirotis. 2008. Get another label? improving data quality and data mining using multiple, noisy label- ers. In Proceedings of KDD-2008.", |
| "links": null |
| }, |
| "BIBREF24": { |
| "ref_id": "b24", |
| "title": "Cheap and fast -but is it good? evaluating non-expert annotations for natural language tasks", |
| "authors": [ |
| { |
| "first": "Rion", |
| "middle": [], |
| "last": "Snow", |
| "suffix": "" |
| }, |
| { |
| "first": "O'", |
| "middle": [], |
| "last": "Brendan", |
| "suffix": "" |
| }, |
| { |
| "first": "Daniel", |
| "middle": [], |
| "last": "Connor", |
| "suffix": "" |
| }, |
| { |
| "first": "Andrew", |
| "middle": [ |
| "Y" |
| ], |
| "last": "Jurafsky", |
| "suffix": "" |
| }, |
| { |
| "first": "", |
| "middle": [], |
| "last": "Ng", |
| "suffix": "" |
| } |
| ], |
| "year": 2008, |
| "venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning", |
| "volume": "", |
| "issue": "", |
| "pages": "", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. Cheap and fast -but is it good? evaluating non-expert annotations for natural language tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Process- ing and Computational Natural Language Learning (EMNLP-2008).", |
| "links": null |
| }, |
| "BIBREF25": { |
| "ref_id": "b25", |
| "title": "Questions and exhaustivity in update semantics", |
| "authors": [ |
| { |
| "first": "Henk", |
| "middle": [], |
| "last": "Zeevat", |
| "suffix": "" |
| } |
| ], |
| "year": 1994, |
| "venue": "Proceedings of the International Workshop on Computational Semantics", |
| "volume": "", |
| "issue": "", |
| "pages": "211--221", |
| "other_ids": {}, |
| "num": null, |
| "urls": [], |
| "raw_text": "Henk Zeevat. 1994. Questions and exhaustivity in up- date semantics. In Harry Bunt, Reinhard Muskens, and Gerrit Rentier, editors, Proceedings of the In- ternational Workshop on Computational Semantics, pages 211-221.", |
| "links": null |
| } |
| }, |
| "ref_entries": { |
| "FIGREF0": { |
| "uris": null, |
| "text": "(6) A: Are you bitter?B: I'm not bitter because I'm a soldier.", |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF1": { |
| "uris": null, |
| "text": "Design of the Mechanical Turk experiment.", |
| "type_str": "figure", |
| "num": null |
| }, |
| "FIGREF3": { |
| "uris": null, |
| "text": "Decision procedure for using the word frequencies across rating categories in the review corpus to decide what a given answer conveys. described in figure 3.", |
| "type_str": "figure", |
| "num": null |
| }, |
| "TABREF1": { |
| "num": null, |
| "text": "Types of question-answer pairs, and counts in the corpus.", |
| "html": null, |
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| "type_str": "table" |
| }, |
| "TABREF2": { |
| "num": null, |
| "text": "", |
| "html": null, |
| "content": "<table><tr><td>: Mean entropy values and standard devi-</td></tr><tr><td>ation obtained in the Mechanical Turk experiment</td></tr><tr><td>for each question-answer pair category.</td></tr><tr><td>(4) A: Have you been living there very long?</td></tr><tr><td>B: I'm in here right now about twelve and</td></tr><tr><td>a half years.</td></tr></table>", |
| "type_str": "table" |
| }, |
| "TABREF4": { |
| "num": null, |
| "text": "Numbers of reviews, words and vocabulary size per rating category in the IMDB review corpus, as well as the average number of words per review.", |
| "html": null, |
| "content": "<table/>", |
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| "TABREF5": { |
| "num": null, |
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| "content": "<table><tr><td colspan=\"5\">: Summary of precision and recall (%) by</td></tr><tr><td>type.</td><td/><td/><td/><td/></tr><tr><td/><td colspan=\"4\">Response Precision Recall F1</td></tr><tr><td>I</td><td>Yes</td><td>87</td><td>76</td><td>81</td></tr><tr><td/><td>No</td><td>57</td><td>71</td><td>63</td></tr><tr><td>II</td><td>Yes</td><td>100</td><td>36</td><td>53</td></tr><tr><td/><td>Uncertain</td><td>67</td><td>40</td><td>50</td></tr></table>", |
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| } |
| } |
