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Learning Subjective Adjectives From Corpora

Learning Subjective Adjectives From Corpora Janyce M. Wiebe New Mexico State University Office of Naval Research grant N00014-95-1-0776. Introduction Learning evaluation and opinion clues Distributional similarity process Refinement with lexical features Improved results from both

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Learning Subjective Adjectives From Corpora

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  1. Learning Subjective Adjectives From Corpora Janyce M. Wiebe New Mexico State University Office of Naval Research grant N00014-95-1-0776.

  2. Introduction • Learning evaluation and opinion clues • Distributional similarity process • Refinement with lexical features • Improved results from both

  3. Subjectivity Tagging Recognizing opinions and evaluations (Subjective sentences) as opposed to material objectively presented as true (Objective sentences) Banfield 1985, Fludernik 1993, Wiebe 1994

  4. Examples • At several different levels, it’s a fascinating tale. subjective • Bell Industries Inc. increased its quarterly to 10 cents from 7 cents a share. objective

  5. “Enthused” “Wonderful!” “Great product” ? “Speculated” “Maybe” Types “Complained” “You Idiot!” “Terrible product”

  6. “Enthused” “Wonderful!” “Great product” Subjectivity ? “Complained” “You Idiot!” “Terrible product” “Speculated” “Maybe”

  7. Subjectivity Same word, different types “Great majority” objective “Great!“ positive evaluative “Just great.” negative evaluative

  8. Subjectivity Multiple types, sources, targets “It’s the best!”, he gushed. - + Writer He It

  9. R [ 13: Julia ] Re: BILL WARRINER!!!! R [ 19: Suzanne ] Re: BILL WARRINER!!!! RS < 16: Suzanne > Re: BILL WARRINER!!!! R [ 26: Doug Bone & Jacqui D] Re: A bin full of buds R [ 24: Karin Adamczyk ] Rose hips? R [ 88: Colette Tremblay ] Re: Rose hips? (long) R [ 8: Karin Adamczyk ] R [ 29: Kay Cangemi ] Re: Rose hips? R [ 23: Karin Adamczyk ] R [ 30: Karin Adamczyk ] R [ 18: BCD ] Re: red(as in wine red) roses R [ 32: Laura Johnson-Kelly ] RS [ 3: PattReck ] R [ 27: Bugman ] Re: BILL WARRINER!!!! R [ 37: Bill ] R < 41: Celeste > Applications: Flame Recognition

  10. Review Mining From: Hoodoo>hoodooBUGZAPPER@newnorth.net> Newsgroups: rec.gardens Subject: Re: Garden software I bought a copy of Garden Encyclopedia from Sierra. Well worth the time and money.

  11. Information Extraction Northwest Airlines settled the remaining lawsuits, a federal judge said. objective “The cost of health care is eroding our standard of living and sapping industrial strength,” complains Maher. subjective

  12. Other Applications • Clustering documents by ideology • Text summarization • Style in machine translation and generation

  13. Overview • Identify large set of candidate clues • Existing resources are not sufficient • Not consistently marked for subjectivity • Not customized to the genre • Learn lexical clues from corpora

  14. Subjectivity tags assigned by multiple annotators to 1001 WSJ sentences Tags representing consensus opinions obtained with EM Corpus and Annotation Wiebe et al. 1999; Bruce & Wiebe 1999

  15. Adjectives • Classifications correlated with adjectives • Adjectives extracted from annotations They promised [e+ 2 yet] more for [e+ 3 reallygood] [e? 1 stuff]. "It's [e? 3 really] [e- 3 bizarre]," says Albert Lerman, creative director at the Wells agency.

  16. R2 R3 I have a brown dog R1 R4 Lin’s Distributional Similarity Word R W I R1 have have R2 dog brown R3 dog . . . Lin 1998

  17. Lin’s Distributional Similarity Word1 Word2 R W R W R W R W R W R W R W R W R W R W R W R W

  18. Bizarre strange similar scary unusual fascinating interesting curious tragic different contradictory peculiar silly sad absurd poignant crazy funny comic compelling odd

  19. Bizarre strange similar scary unusual fascinating interesting curious tragic different contradictory peculiar silly sad absurd poignant crazy funny comic compelling odd

  20. Bizarre strange similar scary unusual fascinating interesting curious tragic different contradictory peculiar silly sad absurd poignant crazy funny comic compelling odd

  21. Bizarre strange similar scary unusual fascinating interesting curious tragic different contradictory peculiar silly sad absurd poignant crazy funny comic compelling odd

  22. Good bad better best nice poor terrific great decent lousy dismal excellent positive exciting fantastic marvelous strong important dumb fair healthy

  23. Good bad better best nice poor terrific great decent lousy dismal excellent positive exciting fantastic marvelous strong important dumb fair healthy

  24. 9 10 Experiments

  25. Seeds + Similar Words 9 10 Experiments Distributional similarity Separate corpus Seeds

  26. Seeds + Similar Words 9 10 p(subjective | s) S Words Experiments Distributional similarity Separate corpus Seeds Filtering S > Adj > Majority

  27. Lexical features • Polarity and Gradability • Learned from corpora • Statistical processing informed by linguistic insights • Different data sets used

  28. Norm Large Larger Largest Gradability * More additional * Very additional Hatzivassiloglou & Wiebe 2000

  29. + polarity beautiful object - polarity ugly Polarity Corrupt and brutal * Corrupt but brutal Hatzivassiloglou & McKeown 1997

  30. Separate corpus Seeds + Similar Words 9 10 Experiments Distributional similarity Seeds Filtering

  31. Separate corpus Separate corpus Seeds + Similar Words 9 10 Experiments Distributional similarity Seeds Filtering Lexical Classification

  32. Lex Seed Lex Pol+ + 4.6 +10.8 Pol- +18.5 +18.7 Pol+,- + 8.5 +11.8 Grad+ + 6.5 +15.3 Results Seed +7.5

  33. Results Lex Seed Lex Pol+,Grad+ + 6.4 +18.0 Pol-, Grad+ +19.9 +21.4 Pol+,- Grad+ + 8.4 +18.2 Seed +7.5

  34. Future Work • Apply process to Netnews and Listservs • Apply word-sense disambiguation techniques to potentially subjective expressions • Flame recognition and review mining

  35. Conclusions • Learning linguistic knowledge from corpora for a pragmatic task • Linguistic information • Manual annotation • Linguistic constraints • Processes improve each other

  36. Application 1: Flame recognition From: pattreck@aol.com (PattReck) Newsgroups: rec.gardens.roses Subject: Re: red(as in wine red) roses My two favorite old reds: Cramoisi Superieure, especially great climbing, and Francis Dubreuil. Also Prospero does well in southern California - aren't you on the west coast? -- Candace

  37. Flames (continued) From: Suzanne <Suzanne_member@newsguy.com> Newsgroups: rec.gardens.roses Subject: Re: BILL WARRINER!!!! >>Wow. You guys are really working poor Suzanne over. >po thang. I thank she been workin over her bottle of Kahlua. ***Up &^%$!!! I've been working at a *job* - no Kahlua! You are a snow-snorting dust-bowl dweller, the dustiest of the dusties. Bill Bradley has the support of the "environmentalists" ha ha ha!

  38. Likely likely possible willing probable receptive unlikely able logical rumored potential counterproductive moot significant hesitant worthy unwilling probably desirable weak forthcoming imminent

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