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1 Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Cornell University Department of Computer Science.

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Presentation on theme: "1 Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Cornell University Department of Computer Science."— Presentation transcript:

1 1 Determining the Hierarchical Structure of Perspective and Speech Expressions Eric Breck and Claire Cardie Cornell University Department of Computer Science

2 Cornell University Computer Science COLING 20042 Events in the News

3 Cornell University Computer Science COLING 20043 Reporting events

4 Cornell University Computer Science COLING 20044 Reporting in text Clapp sums up the environmental movement’s reaction: “The polluters are unreasonable’’ Charlie was angry at Alice’s claim that Bob was unhappy

5 Cornell University Computer Science COLING 20045 Perspective and Speech Expressions (pse’s) A perspective expression is text denoting an explicit opinion, belief, sentiment, etc. The actor was elated that … John’s firm belief in … A speech expression is text denoting spoken or written communication … argued the attorney... … the 9/11 Commission’s final report …

6 Cornell University Computer Science COLING 20046 Grand Vision angryclaim (implicit) unhappy Charlie was angry at Alice’s claim that Bob was unhappy writer CharlieAlice Bob that Bob was unhappy

7 Cornell University Computer Science COLING 20047 This Work angryclaim (implicit) unhappy

8 Cornell University Computer Science COLING 20048 System Output: Pse Hierarchy Charlie was angry at Alice’s claim that Bob was unhappy 78% accurate! angryclaim (implicit) unhappy

9 Cornell University Computer Science COLING 20049 Related Work: Abstract Bergler, 1993 Lexical semantics of reporting verbs Gerard, 2000 Abstract model of news reader

10 Cornell University Computer Science COLING 200410 Related Work: Concrete Bethard et al., 2004 Extract propositional opinions & holders Wiebe, 1994 Tracks “point of view” in narrative text Wiebe et al., 2003 Preliminary results on pse identification Gildea and Jurafsky, 2002 Semantic Role ID - use for finding sources?

11 Cornell University Computer Science COLING 200411 Baseline 1: Only filter through writer Only 66% correct angryclaim (implicit) unhappy

12 Cornell University Computer Science COLING 200412 Baseline 2: Dependency Tree 72% correct angry (implicit) claim unhappy claim unhappy claim unhappy

13 Cornell University Computer Science COLING 200413 A Learning Approach How do we cast the recovery of hierarchical structure as a learning problem? Simplest solution Learn pairwise attachment decisions Is pse parent the parent of pse target ? Combine decisions to form tree Other solutions are possible (n-ary decisions, tree-modeling, etc.)

14 Cornell University Computer Science COLING 200414 Training instances angryclaim (implicit) unhappy

15 Cornell University Computer Science COLING 200415 Training instances angryclaim (implicit) unhappy

16 Cornell University Computer Science COLING 200416 Training instances angryclaim (implicit) unhappy

17 Cornell University Computer Science COLING 200417 Training instances angryclaim (implicit) unhappy

18 Cornell University Computer Science COLING 200418 Training instances angryclaim (implicit) unhappy

19 Cornell University Computer Science COLING 200419 Training instances angryclaim (implicit) unhappy

20 Cornell University Computer Science COLING 200420 Training instances angryclaim (implicit) unhappy

21 Cornell University Computer Science COLING 200421 Decision Combination (implicit) angry claim unhappy

22 Cornell University Computer Science COLING 200422 Decision Combination angry (implicit) angry 0.9 0.1 claim unhappy

23 Cornell University Computer Science COLING 200423 Decision Combination angry (implicit) claim unhappy

24 Cornell University Computer Science COLING 200424 Decision Combination angry claim (implicit) claim 0.5 0.4 0.3 unhappy

25 Cornell University Computer Science COLING 200425 Decision Combination angryclaim (implicit) unhappy

26 Cornell University Computer Science COLING 200426 Decision Combination angryclaim (implicit) unhappy 0.7 0.5 0.2

27 Cornell University Computer Science COLING 200427 Decision Combination angryclaim (implicit) unhappy

28 Cornell University Computer Science COLING 200428 Features(1) All features based on error analysis Parse-based features Domination+ variants Positional features Relative position of pse parent and pse target

29 Cornell University Computer Science COLING 200429 Features(2) Lexical features writer’s implicit pse “said” “according to” part of speech Genre-specific features Charlie, she noted, dislikes Chinese food. “Alice disagrees with me,” Bob said.

30 Cornell University Computer Science COLING 200430 Resources GATE toolkit (Cunningham et al, 2002) - part-of-speech, tokenization, sentence boundaries Collins parser (1999) - extracted dependency parses CASS partial parser (Abney, 1997) IND decision trees (Buntine, 1993)

31 Cornell University Computer Science COLING 200431 Data From the NRRC Multi-Perspective Question Answering workshop (Wiebe, 2002) 535 newswire documents (66 for development, 469 for evaluation) All pse’s annotated, along with sources and other information Hierarchical pse structure annotated for each sentence*

32 Cornell University Computer Science COLING 200432 Example (truncated) model One learned tree, truncated to depth 3: pse 0 is parent of pse 1 iff pse 0 is (implicit) And pse 1 is not in quotes OR pse 0 is said Typical trees on development data: Depth ~20, ~700 leaves

33 Cornell University Computer Science COLING 200433 Evaluation Dependency-based metric (Lin, 1995) Percentage of pse’s whose parents are identified correctly Percentage of sentences with perfectly identified structure Performance of binary classifier

34 Cornell University Computer Science COLING 200434 Results

35 Cornell University Computer Science COLING 200435 Error Analysis Pairwise decisions prevent the model from learning larger structure Speech events and perspective expressions behave differently Treebank-style parses don’t always have the structure we need

36 Cornell University Computer Science COLING 200436 Future Work Identify pse’s Identify sources Evaluate alternative structure-learning methods Use the structure to generate perspective-oriented summaries

37 Cornell University Computer Science COLING 200437 Conclusions Understanding pse structure is important for understanding text Automated analysis of pse structure is possible

38 Cornell University Computer Science COLING 200438 Thank you!

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