Reference Resolution. Sue bought a cup of coffee and a donut from Jane. She met John as she left. He...

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.

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.

Singular Plural Unspecified

She,her, he, him, his, it

We, us, they, them

you

Person and Case Agreement

First Second Third

Nominative I,we you he,she,they

Accusative me,us you Him,her,them

Genitive my,our your His,her, their

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 inJohn 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

Preference Weight

Sentence recency 100

Subject emphasis 80

Existential emphasis 70

Accusitive (direct object) emphasis 80

Indirect object, oblique compliment 40

Non-adverbial emphasis 50

Head noun emphasis 80

Lappin & Leass in action

• Sue read a book on peanuts at the library.

Recency : 100Object : 50Head Noun : 80Non-Adverbial: 50

Recency : 100Object : 50Non-Adverbial: 50

Recency : 100Head Noun : 80Non-Adverbial: 50

Hobbs Tree Search Algorithm

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

Hobbs in Detail

1. Begin at NP2. Go up tree to first NP or S. Call this X, and the path

p.3. 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

4. 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.5. From node X, go up the tree to the first NP or S. Call

it X, and the path p.6. If X is an NP and the path to X did not pass through

the nominal that X dominates, propose X as antecedent

7. 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

8. 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]

CNH Model

• P( a(p) ) = a | p, h, W, t, l, s, d, M) = P(a|M)P(dh | a) P(W | h,t,l,a) P(p|wa)

– P(a|ma) = # of mentions

– P(dh | a) = distance as measured by Hobbs algorithm

– P(W|h,t,l,a) = judges emphasis in the sentence

– P(p| wa) = integrates number, animacy, gender