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Emily Pitler, Annie Louis, Ani Nenkova University of Pennsylvania
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I am in Singapore, but I live in the United States. ◦ Explicit Comparison The main conference is over Wednesday. I am staying for EMNLP. ◦ Implicit Comparison 3
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I am here because I have a presentation to give at ACL. ◦ Explicit Contingency I am a little tired; there is a 13 hour time difference. ◦ Implicit Contingency 4
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Focus on implicit discourse relations ◦ in a realistic distribution Better understanding of lexical features ◦ Showed do not capture semantic oppositions Empirical validation of new and old features ◦ Polarity, verb classes, context, and some lexical features indicate discourse relations 5
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Classify both implicits and explicits ◦ Same sentence [Soricut and Marcu, 2003] ◦ Graphbank corpus: doesn’t distinguish implicit and explicit [ Wellner et al., 2006] Create artificial implicits by deleting connective ◦ I am in Singapore, but I live in the United States. ◦ [Marcu and Echihabi, 2001; Blair-Goldensohn et al., 2007; Sporleder and Lascarides, 2008] 6
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Most basic feature for implicits I_there, I_is, …, tired_time, tired_difference 8 IamaIittletired thereisa13hourtimedifference Marcu and Echihabi, 2001
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The recent explosion of country funds mirrors the “closed-end fund mania of the 1920s, Mr. Foot says, when narrowly focused funds grew wildly popular. They fell into oblivion after the 1929 crash. 9
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Using just content words reduces performance (but has steeper learning curve) ◦ Marcu and Echihabi, 2001 Nouns and adjectives don’t help at all ◦ Lapata and Lascarides, 2004 Filtering out stopwords lowers results ◦ Blair-Goldensohn et al., 2007 10
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Synthetic implicits: Cause/Contrast/None sentences ◦ Explicit instances from Gigaword with connective deleted ◦ Because Cause, But Contrast ◦ At least 3 sentences apart None ◦ Blair-Goldensohn et al., 2007 Random selection ◦ 5,000 Cause ◦ 5,000 Other Computed information gain of word pairs 11
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The government says it has reached most isolated townships by now, but because roads are blocked, getting anything but basic food supplies to people remains difficult. but because Comparison but because Contingency 12
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Maybe even with lots and lots of data, we won’t see “popular…but…oblivion” that often What are we trying to get at? PopularDesirableMollify Oblivion AbhorrentEnrage 13
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Multi-perspective Question Answering Opinion Corpus ◦ Wilson et. al, 2005 Sentiment words annotated as ◦ Positive ◦ Negative ◦ Both ◦ Neutral 15
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Similar to word pairs, but words replaced with polarity tags Arg1: Executives at Time Inc. Magazine Co., a subsidiary of Time Warner, have said the joint venture with Mr. Lang wasn’t a good one. Arg2: The venture, formed in 1986, was supposed to be Time’s low- cost, safe entry into women’s magazines. Arg1NegatePositiveArg2Positive 16
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General Inquirer lexicon ◦ Stone et al., 1966 ◦ Semantic categories of words Complementary classes ◦ “Understatement” vs. “Overstatement” ◦ “Rise” vs. “Fall” ◦ “Pleasure” vs. “Pain” Features ~ Tag pairs, only verbs 17
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Newsweek's circulation for the first six months of 1989 was 3,288,453, flat from the same period last year U.S. News' circulation in the same time was 2,303,328, down 2.6% Probably WSJ-specific 18
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Levin verb class level in LCS database ◦ Levin, 1993; Dorr, 2001 ◦ More related verbs ~ Expansion Average length of verb chunk ◦ They [are allowed to proceed] ~ Contingency ◦ They [proceed] ~ Expansion, Temporal POS tags of the main verb ◦ Same tense ~ Expansion ◦ Different tense ~ Contingency, Temporal 19
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Prior work found first and last words very helpful in predicting sense ◦ Wellner et al., 2006 ◦ Often explicit connectives 20
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Was preceding/following relation explicit? ◦ If so, which sense? ◦ If so, which connective? Does Arg1 begin a paragraph? 21
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Largest available annotated corpus of discourse relations ◦ Penn Treebank WSJ articles ◦ 16,224 implicit relations between adjacent sentences I am a little tired; [because] there is a 13 hour time difference. ◦ Contingency.cause.reason 22
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Relation Sense Proportion of implicits Expansion53% Contingency26% Comparison15% Temporal 6% 23
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Developed features on sections 0-1 Trained on sections 2-20 Tested on sections 21-22 Binary classification task for each sense Trained on equal numbers of positive and negative examples Tested on natural distribution Naïve Bayes classifier 24
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Motivation in prior work ◦ Train on synthetic implicits 26 17.13 31.10 20.96 43.79 21.96 45.60 What works better ◦ Train on actual implicits Synthetic examples can still help! Comp. Cont. ◦ With only best features selected from synthetic implicits
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Featuresf-score First-Last, First321.01 Context19.32 Money/Percent/Num19.04 Random9.91 27 Polarity is actually the worst feature 16.63
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Comparison Not Comparison Positive-Negative or Negative-Positive Pairs 30%31% 28
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Featuresf-score First-Last, First336.75 Verbs36.59 Context29.55 Random19.11 29
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Featuresf-score Polarity Tags71.29 Inquirer Tags70.21 Context67.77 Random64.74 30 Expansion is majority class precision more problematic than recall These features all help other senses
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Featuresf-score First-Last, First315.93 Verbs12.61 Context12.34 Random5.38 31 Temporals often end with words like “Monday” or “yesterday”
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Comparison ◦ Selected word pairs Contingency ◦ Polarity, Verb, First/Last, Modality, Context, Selected word pairs 32
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Expansion ◦ Polarity, Inquirer Tags, Context Temporal ◦ First/Last+word pairs 33
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Comparison 21.96 (17.13) Contingency 47.13 (31.10) Expansion 76.41 (63.84) Temporal 16.76 (16.21) 34 Comparison/Contingency baseline: synthetic implicits word pairs Expansion/Temporal baseline: real implicits word pairs
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Results from classifying each relation independently ◦ Naïve Bayes, MaxEnt, AdaBoost Since context features were helpful, tried CRF 6-way classification, word pairs as features ◦ Naïve Bayes accuracy: 43.27% ◦ CRF accuracy: 44.58% 35
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Focus on implicit discourse relations ◦ in a realistic distribution Better understanding of word pairs ◦ Showed do not capture semantic oppositions Empirical validation of new and old features ◦ Polarity, verb classes, context, and some lexical features indicate discourse relations 36
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