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COMPUTATIONAL APPROACHES TO REFERENCE

Massimo Poesio: Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language. COMPUTATIONAL APPROACHES TO REFERENCE. Jeanette Gundel & Massimo Poesio LSA Summer Institute. The syllabus.

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COMPUTATIONAL APPROACHES TO REFERENCE

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  1. Massimo Poesio: Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language COMPUTATIONAL APPROACHES TO REFERENCE Jeanette Gundel &Massimo Poesio LSA Summer Institute

  2. The syllabus • Week 1: Linguistic and psychologic evidence concerning referring expressions and their interpretation • Week 2: The interpretation of pronouns • Week 3: Definite descriptions and demonstratives

  3. Today • Linguistic evidence about demonstratives (Jeanette) • Poesio and Modjeska: Corpus-based verification of the THIS-NP Hypothesis • The Byron algorithm

  4. Massimo Poesio: Good morning. In this presentation I am going to summarize my research interests, at the intersection of cognition, computation, and language RESOLVING DEMONSTRATIVES: THE PHORA ALGORITHM LSA Summer Institute July 17th, 2003

  5. This lecture • Pronouns and demonstratives are different • Pronoun resolution algorithms for demonstratives • PHORA • Evaluation

  6. Pronouns vs. demonstratives, recap of the facts Put the apple on the napkin and then move it to the side. Put the apple on the napkin and then move that to the side. John thought about {becoming a bum}. It would hurt his mother and it would make his father furious. It would hurt his mother and that would make his father furious. (Schuster, 1988)

  7. `Uniform’ pronoun resolution algorithms: LRC (Tetreault, 2001)

  8. PHORA (Byron, 2002) • Resolves both personal and demonstrative pronouns • Using SEPARATE algorithms (cfr. Sidner, 1979)

  9. The Discourse Model, I: Mentioned Entities and Activated Entities • Discourse model contains TWO separated lists of objects • MENTIONED ENTITIES: interpretation of NPs • One for each referring expression: proper names, descriptive NPs, demonstrative pronouns, 3rd person personal, possessive, and reflexive • ACTIVATED ENTITIES: • Each clause may evoke more than one PROXY for linguistic constituents other than NPs • REFERRING FUNCTIONS are used to extract interpretation

  10. The Discourse Model, II • Mentioned DEs remain in DM for the entire discourse • Activated Des updated after every CLAUSE • FOCUS is the first-mentioned entity of a clause; updated after each clause

  11. Examples of Proxies

  12. Outline of the algorithm • Build discourse proxies for Discourse Unit n (DUn) • For each pronoun p in DUn+1 • Calculate the MOST GENERAL SEMANTIC TYPE T that satisfies the constraints on the predicate argument position the pronoun occurs in • Find a referent that matches p using different search orders for personal and demonstrative pronouns

  13. The importance of semantic context • Often the predicate imposes strong constraints on the type of objects that may serve as antecedents of the pronoun • Eckert and Strube (2000): I-INCOMPATIBLE vs. A-INCOMPATIBLE • That’s right • Let me help you lifting THAT

  14. Semantic Constraints in PHORA • VERB’s SELECTIONAL RESTRICTIONS: • Load THEM into the boxcar  CARGO(X) • PREDICATE NPs: force same type interpret. • That’s a good route  ROUTE(X) • PREDICATE ADJECTIVES: • It’s right  CORRECT(X)  PROPOSITION(X) • NO CONSTRAINT: • That’s good  ACCEPTABLE(X)

  15. Searching for antecedents • Choose as antecedent the first DE that satisfies agreement features and semantic constraints for the pronoun, searching in different orders for personal and demonstrative pronouns.

  16. Search: Personal pronouns • Mentioned entities to the left of the pronoun in the current clause, DUn+1, right-to-left • The focused entity of DUn • The remaining mentioned entities, going backwards one clause at a time, and then left-to-right in the clause • Activated entities in DUn

  17. Search: Demonstrative pronouns • Activated entities in DUn • The focused entity of Dun (only if it can be coerced into a kind) • The remaining mentioned entities, going backwards one clause at a time, and then left-to-right in the clause

  18. Referring Functions (Nunberg 1979, Webber 1988/1990)

  19. Example, G0 G0: Engine 1 goes to Avon to get the oranges.

  20. Example, G1a, I G1a: So it’ll get there at 3pm. LF: (ARRIVE :theme x :dest y :time z)SEM CONSTRAINTS: MOVABLE-OBJECT(X)CANDIDATES: ENG1, ORANGESSEARCH: ENG1 produced first

  21. Example, G1a, II – Discourse Model update

  22. Example, G1b G1b: that takes two hours. LF: (TAKE-TIME :theme x :cost y)SEM CONSTRAINTS: EVENT(X)SEARCH: Try first ACTIVATED entities of G1a, using referring function event(d)event(‘All of G1a’) is defined  search succeeds

  23. Example, G1c G1c: that’s where the orange warehouse is. LF: (EQUAL :theme x :complement y)SEM CONSTRAINTS: LOCATION(X)SEARCH: 1. ACTIVATED entities of G1b: no location 2. MENTIONED entities of G1b: nothing 3. Mentioned entities of G1a: AVON satisfies constraint  search succeeds

  24. Evaluation, I

  25. Evaluation, II

  26. References • M. Eckert and M. Strube .2000. Dialogue Acts, synchronizing units, and anaphora resolution. Journal of Semantics, 17(1). • B. Webber. Structure and ostension in the interpretation of deixis. Language and Cognitive Processes, 1990.

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