Reference Resolution

Reference Resolution

Sue bought a cup of coffee and a donut from Jane. She met John as she left. He looked at her enviously as she drank the coffee. It was delicious.

Sue bought a cup of coffee and a donut from Jane. She met John as she left. He looked at her enviously as she drank the coffee. It was delicious.

How could a computer resolve these references? • Complete model of dialogue too complicated • Are there any simpler ways?

Hard Constraints on Coreference • Number agreement • Person and case • Gender Agreement • Syntactic Agreement • Selectional Restrictions

Number agreement John and Mary loaned Sue a cup of coffee. Little did they know the magnitude of her addiction.

Person and Case Agreement

Gender Agreement *John has a coffee machine. She loves it.

Syntactic Agreement • Reflexives (himself, herself…) have strong constraints on what syntactic positions they can appear in John bought himself a cup of coffee. *John bought him a cup of coffee.

Selectional Constraints Jim bought a coffee from the store. He drank it quickly.

Also : Preferences • Recency • Grammatical Role • Repeated Mention • Parallelism • Verb Semantics • Based on Salience

Recency John had a pop-tart. Bill had a jelly donut. Mary wanted it. Recent Entities are more salient

Grammatical Role “Sue bought a cup of coffee and a donut from Jane. She met John as she left.” • Entities in subject position are more salient

Repeated Mention John went to the store to buy coffee. He loves coffee. He drinks 5 cups a day. At the store, Bill sold him a cup. He was delighed. • Entities mentioned more frequently are more salient

Parallelism John bought coffee from Jim in the morning. Sue bought coffee from him in the evening. • Even with preferences to the contrary (grammatical role) the syntactic parallelism strongly prefers [him = Jim]

Verb Semantics John telephoned Bill. He was jonesing for coffee. John criticized Bill. He was jonesing for coffee. • Perhaps salience of different elements in the sentence changes with respect to the verb used.

Algorithms --- How to integrate these preferences? • Constraints are easy to use : reject all hypothesis which violate the hard constraints (if you can accurately detect the constraints!) • Preferences more difficult – how can one integrate these different preferences?

Lappin and Leass • Use Weighting Scheme • Weight each mention, cut values in half at each new sentence

Lappin & Leass in action • Sue read a book on peanuts at the library. Recency : 100 Object : 50 Head Noun : 80 Non-Adverbial: 50 Recency : 100 Object : 50 Non-Adverbial: 50 Recency : 100 Head Noun : 80 Non-Adverbial: 50

Hobbs Tree Search Algorithm • Given parse trees, search them in a specific order to find the most likely referent

Hobbs in Detail • Begin at NP • Go up tree to first NP or S. Call this X, and the path p. • Traverse all branches below X to the left of p. Propose as antecedent any NP that has a NP or S between it and X • If X is the highest S in the sentence, traverse the parse trees of the previous sentences in the order of recency. Traverse left-to-right, breadth first. When a NP is encountered, propose as antecedent. If not the highest node, go to step 5.

Hobbs cont. • From node X, go up the tree to the first NP or S. Call it X, and the path p. • If X is an NP and the path to X did not pass through the nominal that X dominates, propose X as antecedent • Traverse all branches below X to the right of the path, in a left-to-right, breadth first manner. Propose any NP encountered as the antecdent • If X is an S node, traverse all brnaches of X to the right of the path but do not go below any NP or S encountered. Propose any NP as the antecedent.

Charniak, Ng and Hale • Statistical integration of preferences • Similar to Lappin Leass • Uses distance (recency constraint) [d], syntactic position (gramatical role emphasis) [s], gender number and animacy [W], verb selectional constraints [l,h], and mention (repeated mention counts) [M]

Reference Resolution