Reference Resolution

1. Reference Resolution Natural Language Processing January 22, 2008

2. Agenda • Reference resolution • Knowledge-rich, deep analysis approaches • Centering • Knowledge-based, shallow analysis: CogNIAC (‘95) • Learning approaches: Fully, Weakly, and Un- Supervised • Cardie&Ng ’99-’04

3. Centering • Identify the local “center” of attention • Pronominalization focuses attention, appropriate use establishes coherence • Identify entities available for reference • Describe shifts in what discourse is about • Prefer different types for coherence

4. Centering: Structures • Each utterance (Un) has: • List of forward-looking centers: Cf(Un) • Entities realized/evoked in Un • Rank by likelihood of focus of future discourse • Highest ranked element: Cp(Un) • Backward looking center (focus): Cb(Un)

5. Centering: Transitions

6. Centering: Constraints and Rules • Constraints: • Exactly ONE backward -looking center • Everything in Cf(Un) realized in Un • Cb(Un): highest ranked item in Cf(Un) in Un-1 • Rules: • If any item in Cf(Un-1) realized as pronoun in Un, Cb(Un) must be realized as pronoun • Transitions are ranked: • Continuing > Retaining > Smooth Shift > Rough Shift

7. Centering: Example • John saw a beautiful Acura Integra at the dealership • Cf: (John, Integra, dealership); No Cb • He showed it to Bill. • Cf:(John/he, Integra/it*, Bill); Cb: John/he • He bought it: • Cf: (John/he, Integra/it); Cb: John/he

8. Reference Resolution: Differences • Different structures to capture focus • Different assumptions about: • # of foci, ambiguity of reference • Different combinations of features

9. Reference Resolution: Agreements • Knowledge-based • Deep analysis: full parsing, semantic analysis • Enforce syntactic/semantic constraints • Preferences: • Recency • Grammatical Role Parallelism (ex. Hobbs) • Role ranking • Frequency of mention • Local reference resolution • Little/No world knowledge • Similar levels of effectiveness

10. Alternative Strategies • Knowledge-based, but • Shallow processing, simple rules! • CogNIAC (Baldwin ’95) • Data-driven • Fully or weakly supervised learning • Cardie & Ng ( ’02-’04)

11. Questions • 80% on (clean) text. What about… • Conversational speech? • Ill-formed, disfluent • Dialogue? • Multiple speakers introduce referents • Multimodal communication? • How else can entities be evoked? • Are all equally salient?

12. More Questions • 80% on (clean) (English) text: What about.. • Other languages? • Salience hierarchies the same • Other factors • Syntactic constraints? • E.g. reflexives in Chinese, Korean,.. • Zero anaphora? • How do you resolve a pronoun if you can’t find it?

13. CogNIAC • Goal: Resolve with high precision • Identify where ambiguous, use no world knowledge, simple syntactic analysis • Precision: # correct labelings/# of labelings • Recall: # correct labelings/# of anaphors • Uses simple set of ranked rules • Applied incrementally left-to-right • Designed to work on newspaper articles • Tune/rank rules

14. CogNIAC: Rules • Only resolve reference if unique antecedent • 1) Unique in prior discourse • 2) Reflexive: nearest legal in same sentence • 3) Unique in current & prior: • 4) Possessive Pro: single exact poss in prior • 5) Unique in current • 6) Unique subj/subj pronoun

15. CogNIAC: Example • John saw a beautiful Acura Integra in the dealership. • He showed it to Bill. • He= John : Rule 1; it -> ambiguous (Integra) • He bought it. • He=John: Rule 6; it=Integra: Rule 3

16. Data-driven Reference Resolution • Prior approaches • Knowledge-based, hand-crafted • Data-driven machine learning approach • Cast coreference as classification problem • For each pair NPi,NPj, do they corefer? • Cluster to form equivalence classes

17. NP Coreference Examples • Link all NPs refer to same entity Queen Elizabeth set about transforming herhusband, King George VI, into a viable monarch. Logue, a renowned speech therapist, was summoned to help the King overcome hisspeech impediment... Example from Cardie&Ng 2004

18. Training Instances • 25 features per instance: 2NPs, features, class • lexical (3) • string matching for pronouns, proper names, common nouns • grammatical (18) • pronoun_1, pronoun_2, demonstrative_2, indefinite_2, … • number, gender, animacy • appositive, predicate nominative • binding constraints, simple contra-indexing constraints, … • span, maximalnp, … • semantic (2) • same WordNet class • alias • positional (1) • distance between the NPs in terms of # of sentences • knowledge-based (1) • naïve pronoun resolution algorithm

19. Classification & Clustering • Classifiers: • C4.5 (Decision Trees), RIPPER • Cluster: Best-first, single link clustering • Each NP in own class • Test preceding NPs • Select highest confidence coref, merge classes • Tune: Training sample skew: class, type

20. Classifier for MUC-6 Data Set

21. Unsupervised Clustering • Analogous features to supervised • Distance measure: weighted sum of features • Positive infinite weights: block clustering • Negative infinite weights: cluster, unless blocked • Others, heuristic • If distance > r (cluster radius), non-coref • Clustering: • Each NP in own class • Test each preceding NP for dist < r • If so, cluster, UNLESS incompatible NP • Performance: Middling: b/t best and worst

22. Problem 1 • Coreference is a rare relation • skewed class distributions (2% positive instances) • remove some negative instances NP1 NP2 NP3 NP4 NP5 NP6 NP7 NP8 NP9 farthest antecedent

23. Problem 2 • Coreference is a discourse-level problem • different solutions for different types of NPs • proper names: string matching and aliasing • inclusion of “hard” positive training instances • positive example selection: selects easy positive training instances (cf. Harabagiu et al. (2001)) Queen Elizabeth set about transforming herhusband, King George VI, into a viable monarch. Logue, the renowned speech therapist, was summoned to help the King overcome his speech impediment...

24. Problem 3 • Coreference is an equivalence relation • loss of transitivity • need to tighten the connection between classification and clustering • prune learned rules w.r.t. the clustering-level coreference scoring function coref ? coref ? [Queen Elizabeth] set about transforming [her] [husband], ... not coref ?

25. Weakly Supervised Learning • Exploit small pool of labeled training data • Larger pool unlabeled • Single-View Multi-Learner Co-training • 2 different learning algorithms, same feature set • each classifier labels unlabeled instances for the other classifier • data pool is flushed after each iteration

26. Effectiveness • Supervised learning approaches • Comparable performance to knowledge-based • Weakly supervised approaches • Decent effectiveness, still lags supervised • Dramatically less labeled training data • 1K vs 500K

27. Reference Resolution: Extensions • Cross-document co-reference • (Baldwin & Bagga 1998) • Break “the document boundary” • Question: “John Smith” in A = “John Smith” in B? • Approach: • Integrate: • Within-document co-reference • with • Vector Space Model similarity

28. Cross-document Co-reference • Run within-document co-reference (CAMP) • Produce chains of all terms used to refer to entity • Extract all sentences with reference to entity • Pseudo per-entity summary for each document • Use Vector Space Model (VSM) distance to compute similarity between summaries

29. Cross-document Co-reference • Experiments: • 197 NYT articles referring to “John Smith” • 35 different people, 24: 1 article each • With CAMP: Precision 92%; Recall 78% • Without CAMP: Precision 90%; Recall 76% • Pure Named Entity: Precision 23%; Recall 100%

30. Conclusions • Co-reference establishes coherence • Reference resolution depends on coherence • Variety of approaches: • Syntactic constraints, Recency, Frequency,Role • Similar effectiveness - different requirements • Co-reference can enable summarization within and across documents (and languages!)

31. Coherence & Coreference • Cohesion: Establishes semantic unity of discourse • Necessary condition • Different types of cohesive forms and relations • Enables interpretation of referring expressions • Reference resolution • Syntactic/Semantic Constraints/Preferences • Discourse, Task/Domain, World knowledge • Structure and semantic constraints

32. Challenges • Alternative approaches to reference resolution • Different constraints, rankings, combination • Different types of referent • Speech acts, propositions, actions, events • “Inferrables” - e.g. car -> door, hood, trunk,.. • Discontinuous sets • Generics • Time

33. Discourse Structure Theories ,Natural Language Processing CMSC 35100-1 January 22, 2008

34. Roadmap • Goals of Discourse Structure Models • Limitations of early approaches • Models of Discourse Structure • Attention & Intentions (Grosz & Sidner 86) • Rhetorical Structure Theory (Mann & Thompson 87) • Contrasts, Constraints & Conclusions

35. Why Model Discourse Structure?(Theoretical) • Discourse: not just constituent utterances • Create joint meaning • Context guides interpretation of constituents • How???? • What are the units? • How do they combine to establish meaning? • How can we derive structure from surface forms? • What makes discourse coherent vs not? • How do they influence reference resolution?

36. Why Model Discourse Structure?(Applied) • Design better summarization, understanding • Improve speech synthesis • Influenced by structure • Develop approach for generation of discourse • Design dialogue agents for task interaction • Guide reference resolution

37. Early Discourse Models • Schemas & Plans • (McKeown, Reichman, Litman & Allen) • Task/Situation model = discourse model • Specific->General: “restaurant” -> AI planning • Topic/Focus Theories (Grosz 76, Sidner 76) • Reference structure = discourse structure • Speech Act • single utt intentions vs extended discourse

38. Discourse Models: Common Features • Hierarchical, Sequential structure applied to subunits • Discourse “segments” • Need to detect, interpret • Referring expressions provide coherence • Explain and link • Meaning of discourse more than that of component utterances • Meaning of units depends on context

39. Earlier Models • Issues: • Conflate different aspects of discourse • Task plan, discourse plan • Ignore aspects of discourse • Goals & intentions vs focus • Overspecific • Fixed plan, schema, relation inventory

40. Attention, Intentions and the Structure of Discourse • Grosz&Sidner (1986) • Goals: • Integrate approaches for focus (reference res.), plan/task structure, discourse structure, goals • Three part model: • Linguistic structure (utterances) • Attentional structure (focus, reference) • Intentional structure (plans, purposes)

41. Linguistic Structure • Utterances group into discourse segments • Hierarchical, not necessarily contiguous • Not strictly decompositional • 2-way interactions • Utterances define structure; • Cue phrases mark segment boundaries • But, okay, fine, incidentally • Structure guides interpretation • Reference

42. Intentional Structure • Discourse & participants: overall purpose • Discourse segments have purposes (DP/DSP) • Contribute to overall • Main DP/DSP intended to be recognized

43. Intentional Structure: Relations • Two relations between purposes • Dominance • DSP1 dominates DSP2 if doing DSP2 contributes to achieving DSP1 • Satisfaction-Precedence • DSP1 must be satisfied before DSP2 • Purposes: • Intend that someone know something, do something, believe something, etc • Open-ended

44. Attentional State • Captures focus of attention in discourse • Incremental • Focus Spaces • Include entities salient/evoked in discourse • Include a current DSP • Stack-structured: • higher->more salient, lower still accessible • Push:segment contributes to previous DSP • Pop: segment to contributes to more dominant DSP • Tied to intentional structure

45. Attentional State cntd. • Focusing structure depends on the intentional structure: the relationships between DSPs determine pushes and pops from the stack • Focusing structure coordinates the linguistic and intentional structures during processing • Like the other 2 structures, focusing structure evolves as discourse proceeds

46. Discourse examples • Essay • Task-oriented dialog • Intentional structure is neither identical nor isomorphic to the general plan

47. 0 The "movies" are so attractive to the great American public, especially to young people, that it is time to take careful thought about their effect on mind and morals. Ought any parent to permit his children to attend a moving picture show often or without being quite certain of the show he permits them to see? No one can deny, of course, that great educational and ethical gains may be made through the movies because of their astonishing vividness. But the important fact to be determined is the total result of continuous and indiscriminate attendance on shows of this kind. Can it other than harmful? In the first place the character of the plays is seldom of the best. One has only to read the ever-present "movie" billboard to see how cheap, melodramatic and vulgar most of the photoplays are. Even the best plays, moreover, are bound to be exciting and over-emotional. Without spoken words, facial expression and gesture must carry the meaning: but only strong emotion or buffoonery can be represented through facial expression and gesture. The more reasonable and quiet aspects of life are necessarily neglected. How can our young people drink in through their eyes a continuous spectacle of intense and strained activity and feeling without harmful effects? Parents and teachers will do well to guard the young against overindulgence in the taste for the "movie". 1 2 3 4 5 6

48. H:1. First you have to remove the flywheel . R:2. How do I remove the flywheel? H:3. First, loosen the screw , then pull it off. R:4. OK .5. The tool I have is awkward. Is there another tool that I could use instead? H:6. Show me the tool you are using. R:7. OK. H:8. Are you sure you are using the right size key? R:9. I’ll try some others. 10. I found an angle I can get at it . 11. The screw is loose, but I’m having trouble getting the flywheel off. H:12. Use the wheelpuller . Do you know how to use it ? R:13. No. H:14. Do you know what it looks like? R:15. Yes. H:16. Show it to me please. R:17. OK. H:18. Good. Loosen the screw in the center and place the jaws around the hub of the flywheel, then tighten the screw onto the center of the shaft. The flywheel should slide off.

49. Processing issues • Intention recognition • What info can be used to recognize an intention • At what point does this info become available • Overall processing module has to be able to operate on partial information • It must allow for incrementally constraining the range of possibilities on the basis of new info that becomes available as the segment progresses

50. Info constraining DSP: • Specific linguistic markers • Utterance-level intentions • General knowledge about actions and objects in the domain of discourse • Applications of the theory: • Interruptions • Weak – not linked to immediate DSP • Strong - not linked to any DSP • Cue words