Pragmatics I Reference resolution Ling 571 Fei Xia

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

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

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.

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

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

Refer (evoke) Refer (access) He John Corefer

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

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)

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)

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.

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

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 20 K for a Camry.

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?

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)

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. –…

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)

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

Refer (evoke) Refer (access) He John Corefer

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

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

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

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.

Semantic constraints • Selectional restrictions – (1) John parked his car in the garage. – (2 a) He had driven it around for hours. – (2 b) 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.

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

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.

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

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

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

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

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.

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

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.

An example • John saw a beautiful Acura at the dealership. John Rec Subj 100 80 Acura 100 dealer 100 ship Obj Nonadv 50 Head Total noun 80 310 50 50 80 280 50 80 230

Before moving on to the 2 nd sentence Referent Value John Referring expressions {John} Acura {Acura} 140 dealership {dealership} 155 115

Handling “He” • He showed it to Bob. • The value of “He” is 310 Referent Value John Referring expressions {John} Acura {Acura} 140 dealership {dealership} 155 115

After adding “he” • He showed it to Bob. Referent Value John Referring expressions {John, he} Acura {Acura} 140 dealership {dealership} 465 115

Handling “it” • He showed it to Bob. • The salience value of “it” is 280. • Two new factors: – Role parallelism: – Cataphora (? ? ): Referent John 35 -175 Expressions {John, he} Acura {Acura} dealership {dealership} Value 465 140 115

After adding “it” • He showed it to Bob. • The salience value of “it” is 280. • Two new factors: – Role parallelism: – Cataphora (? ? ): 35 -175 Referent John Expressions {John, he} Value 465 Acura {Acura, it} 140+280 +35=455 115 dealership {dealership}

Handling “Bob” • He showed it to Bob. • The salience value of “Bob” is 270. Referent John Expressions {John, he} Acura {Acura, it} dealership {dealership} Value 465 455 115

After adding “Bob” • He showed it to Bob. • The salience value of “Bob” is 270. Refere Expres value nt sions John {John, 465 he} Acura {Acura, 455 it} Bob {Bob} 270

Moving on to the 3 rd sentence • He bought it. Referent John Expressions {John, he} value 232. 5 Acura {Acura, it} 227. 5 Bob {Bob} 135 dealership {dealership} 57. 5 He (John) bought it (Acura).

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.

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

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