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Pragmatics I: Reference resolution

Pragmatics I: Reference resolution. Ling 571 Fei Xia Week 7: 11/8/05. Outline. Discourse: a related group of sentences Ex: articles, dialogue, …. Pragmatics: the study of the relation between language and context-of-use Reference resolution Discourse structure. Reference resolution.

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Pragmatics I: Reference resolution

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  1. Pragmatics I: Reference resolution Ling 571 Fei Xia Week 7: 11/8/05

  2. Outline • Discourse: a related group of sentences • Ex: articles, dialogue, …. • Pragmatics: the study of the relation between language and context-of-use • Reference resolution • Discourse structure

  3. Reference resolution

  4. Reference resolution • Some terms: referents, referring expression • Discourse model • Types of referring expression • Types of referents • Constraints and preference for reference resolution • Some algorithms for reference resolution

  5. Some terms • Ex: John bought a book yesterday. He thought it was cheap. • Referring expression: the expression used to refer to an entity: • Ex: John, a book, he, it • Referent: an entity that is referred to.

  6. Some Terms (cont) • Co-reference: two or more referring expressions refer to the same entity: e.g., “John” and “he”. • Antecedents: a referring expression that licenses the use of others. Ex. John • Anaphora: reference to an entity that has been previous introduced. Ex: he

  7. Discourse Model • A discourse model stores the representations of entities that have been referred to in the discourse and the relationships in which they participate. • Two operations: • Evoke: first mention • Access: subsequence mention

  8. Refer (evoke) Refer (access) He John Corefer

  9. Five types of referring expressions • Indefinite NPs: a car • Definite NPs: the car • Pronouns: it • Demonstratives: this, that • One-anaphora: one

  10. Indefinite NPs • Introduce entities that are new to the hearer • The entity may or may not be identifiable to the speaker: • I saw an Acura today. (Specific reading) • I am going to the dealership to buy an Acura today. (specific or non-specific) • I hope that they still have it. (Specific) • I hope that they have a car I like. (non-specific)

  11. Definite NPs • Identifiable to the hearer • I saw an Acura today. The Acura … (explicitly mentioned before in the context) • The Eagles …. (the hearer’s knowledge about the world) • The largest company in Seattle announced … (inherently unique)

  12. Pronouns • Pronouns refer to something that is identifiable to the hearer. • The referent must have a high degree of salience in the discourse model. • Pronouns can participate in cataphora, in which they appear before their referents. • Ex: Before he bought it, John checked over the Acura very carefully.

  13. Demonstratives • Demonstratives refer to something that is identifiable to the hearer. • They are used alone or as a determiner: • Ex: I want this. I want this car. • “this” indicating closeness, “that” signaling distance: spatial/temporal distance.

  14. One-anaphora • “One”  “One of them” • It selects a member from a set that is identifiable to the hearer. • Ex: • He had a BMW before, now he got another one. • Is he the one? • You like this one, or that one? • John has two BMWs, but I have only one. • One should not pay more than 20K for a Camry.

  15. Five types of referring expressions • Indefinite NPs: a car • Definite NPs: the car • Pronouns: it • Demonstratives: this, that • One-anaphora: one Next question: what do a referring expression refers to?

  16. Types of referents • Ex: According to John, Bob bought Sue a BMW, and Sue bought Bob a Honda. • But that turned out to be a lie. (speech act) • But that was false. (proposition) • That caused Bob to become rather poor. (event) • That caused them both to become rather poor. (combination of events)

  17. Inferrables • Explicitly evoked in the text: John bought a car. • Inferrables: inferrentially related to an evoked entity. • Whole-part: I almost bought a BMW today, but a door had a dent and the engine seemed noisy. • The results of action: Mix the flour and water, kneed the dough until smooth. • …

  18. Discontinuous sets • Plural references may refer to entities that have been evoked separately. • Ex: • John has an Acura, and Mary has a Mazda. They drive them all the time. (pairwise reading)

  19. Generics • Generic references: individual  generic • Ex: I saw six BMWs today. They are the coolest cars.

  20. Refer (evoke) Refer (access) He John Corefer

  21. Constraints and preferences for reference resolution • Constraints (filters): • Agreement: number, person, gender • Syntax: reflexives • Semantics: selectional restrictions • Preferences: • Salience • Parallelism • Verb semantics

  22. Agreement • Number: • (1) John bought a BMW. • (2a) It is great. • (2b) They are great. • (2c) ??They are red. • Person: • (1) John and I have BMWs. • (2a) We love them. • (2b) They love them.

  23. Agreement (cont) • Gender: she, he, it. • (1) John looked at the new ship. • (2) She was beautiful. • (1’) Mary looked at the new ship. • (2) She was beautiful.

  24. Syntactic constraints • Reflexives and personal pronouns. • John bought himself a car. • John bought him a car. • John wrapped a blanket around himself. • John wrapped a blanket around him.

  25. Semantic constraints • Selectional restrictions • (1) John parked his car in the garage. • (2a) He had driven it around for hours. • (2b) It is very messy, with old bike and car parts lying around everywhere. • (1) John parked his Acura in downtown Beverly Hills. • (2) It is very messy, with old bikes and car parts lying around everywhere.

  26. Preferences in pronoun interpretation • Saliency: • Recency • Grammatical role • Repeated Mention • Parallelism • Verb semantics

  27. Saliency • Recency: • John has an Integra. …Bill has a BMW. Mary likes to drive it. • Grammatical role: • John went the dealership with Bill. He bought a car. • Repeated mention: • John needed a car. He decided to get a BMW. Bill went to the dealership with him. He bought one.

  28. Parallelism • Mary went with Sue to the Acura dealership. Sally went with her to the Mazda dealership.

  29. Verb semantics • John telephoned Bill. He lost the pamphlet on BMWs. • John seized the pamphlet to Bill. He loves reading about cars. • The car dealer admired John. He knows Acuras inside and out. Thematic roles or world knowledge? criticized passed impressed

  30. Constraints and preferences for reference resolution • Hard-and-fast constraints (filters): • Agreement: number, person, case, gender • Syntax: reflexives • Semantics: selectional restrictions • Preferences: • Saliency: recency, thematic roles, repeated mention • Parallelism • Verb semantics: thematic roles or world knowledge

  31. Algorithms for pronoun resolution • Heuristics approaches: • Lappin & Leass (1994) • Hobbs (1978) • Centering Theory (Grosz, Joshi, Weinstein 1995, and various) • Machine learning approaches

  32. Lappin & Leass 1994 • A heuristic approach. • Use agreement and syntactic constraints. • Represent preferences (saliency, parallelism) with weights. • Not using: selectional restrictions, verb semantics, world knowledge.

  33. Salience factors and weights • Sentence recency: 100 • Subject: 80 • Existential position: 70 • There is a car …. • Direct object: 50 • Indirect object: 40 • Non-adv: 50 • Inside his car, John ….. • Head noun of max NP: 80 • The manual for the car is …

  34. The algorithm • Start with an empty set of referents. • Process each sentence • For each referring expression • Calculate the salience value of the expression. • If it could be merged with existing referents then choose the referent with the highest saliency value else add it as a new referent. • Update the value of the corresponding referent. • Cut the values of all the referents by half.

  35. John saw a beautiful Acura at the dealership. An example

  36. Before moving on to the 2nd sentence

  37. Handling “He” • He showed it to Bob. • The value of “He” is 310

  38. After adding “he” • He showed it to Bob.

  39. Handling “it” • He showed it to Bob. • The salience value of “it” is 280. • Two new factors: • Role parallelism: 35 • Cataphora (??): -175

  40. After adding “it” • He showed it to Bob. • The salience value of “it” is 280. • Two new factors: • Role parallelism: 35 • Cataphora (??): -175

  41. Handling “Bob” • He showed it to Bob. • The salience value of “Bob” is 270.

  42. After adding “Bob” • He showed it to Bob. • The salience value of “Bob” is 270.

  43. Moving on to the 3rd sentence • He bought it.  He (John) bought it (Acura).

  44. Core of the algorithm • For each referring expression • Calculate the saliency value, x. • Collect all the referents that comply with agreement and syntactic constraints. • If the set is not empty, choose the one with the highest salience value, and increase the reference value by x. • If the set is empty, add a new referent to the discourse model, and set its value to x.

  45. Algorithms for reference resolution • Heuristics approaches: • Lappin & Leass (1994) • Hobbs (1978) • Centering Theory (Grosz, Joshi, Weinstein 1995, and various) • Machine learning approaches

  46. Summary of reference resolution • Some terms: referents, referring expression • Discourse model • Types of referring expression • Types of referents • Constraints and preference for reference resolution • Some algorithms for reference resolution

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