Lecture 21

1. Lecture 21 Algorithms for Reference Resolution CS 4705

2. Issues • Which features should we make use of? • How should we order them? I.e. which override which? • What should appear in our discourse model? I.e., what types of information do we need to keep track of? • How to evaluate?

3. Lappin & Leass ‘94 • Weights candidate antecedents by recency and syntactic preference (86% accuracy) • Two major functions to perform: • Update the discourse model when an NP that evokes a new entity is found in the text, computing the salience of this entity for future anaphora resolution • Find most likely referent for current anaphor by considering possible antecedents and their salience values • Partial example for 3P, non-reflexives

4. Salience Factors and Weights (Trained)

5. Implicit ordering of arguments: subj/exist pred/obj/indobj-oblique/dem.advPP On the sofa, the cat was eating bonbons. sofa: 100+40+80=220 cat: 100+80+50+80=310 bonbons: 100+50+50+80=280 • Update: • Weights accumulate over time • Cut in half after each sentence processed • Salience values for subsequent referents accumulate for equivalence class of correferential items (exceptions, e.g. multiple references in same sentence)

6. The bonbons were clearly very tasty. sofa: 140/2=70 cat: 310/2=155 bonbons: 280/2 +(100+80+50+80)=450 • Additional salience weights for grammatical role parallelism (35) and cataphora (-175) calculated when pronoun to be resolved • Additional constraints on gender/number agrmt/syntax They were a gift from an unknown admirer. sofa: 70/2=35 cat: 155/2=77.5 bonbons: 450/2=225 (+35)

7. Reference Resolution • Collect potential referents (up to four sentences back): {sofa,cat,bonbons} • Remove those that don’t agree in number/gender with pronoun {bonbons} • Remove those that don’t pass intra-sentential syntactic coreference constraints The cat washed it. (itcat) • Add applicable values for role parallelism (+35) or cataphora (-175) to current salience value for each potential antecedent • Select referent with highest salience; if tie, select closest referent in string

8. Solution They were a gift from an unknown admirer. (they=bonbons) bonbons: 225/2=112.5 (+35) Why? Only potential antecedent to agree in gender/number Also has highest salience (147.5) Or… It ate them all. (it=?) sofa: 70/2=35 cat: 155/2=77.5 bonbons: 225/2=112.5

9. Centering (Grosz et al ‘95;Brennan et al ‘87) • Adds concept of discourse center • Un: an utterance • Backward-looking center Cb(Un): current focus after Un interpreted • Forward-looking centers Cf(Un): ordered list of potential focii referred to in Un • Cb(Un+1) is highest ranked member of Cf(Un) • Cp(Un): preferred center of Cf(Un) • Other Cf ordered subj<exist. Prednom<obj<indobj-oblique<dem. advPP (Brennan et al)

10. Transitions from Un to Un+1

11. Rules • If any element of Cf(Un) is pronominalized in Un+1, then Cb(Un+1) must be • Preference: Continue > Retain > Smooth-Shift > Rough-Shift • Algorithm • Generate Cb-Cf combinations for all possible reference assignments • Filter by constraints (syntactic coreference, selectional restrictions,…) • Rank by preference among transition orderings

12. Example U1:George saw a mouse on the floor. U2:He showed it to Harry. U3:He ate it. • Grammatical role hierarchy • Cf(U1): {George,mouse,floor} • Cp(U1): George • Cb(U1): undefined • Assume it=mouse • Cf(U2): {George, mouse,Harry} • Cp(U2): George • Cb(U2): George • Continue (Cp(U2)=Cb(U2); Cb(U1) undefined

13. Assume it=floor • Cf(U2): {George,floor,Harry} • Cp(U2): George • Cb(U2): George • Continue (Cp(U2)=Cb(U2); Cb(U1) undefined) • What did George show Harry? • How to break a tie? Ordering of previous Cf list? U3: He ate it. Assume he=George, it=mouse • Cf(U3): {George,mouse} • Cp(U3): George • Cb(U3): George • Continue (Cp(U3)=Cb(U3); Cb(U3)=Cb(U2))

14. Assume he=Harry, it=mouse • Cf(U3): {Harry,mouse} • Cp(U3): Harry • Cb(U3): Harry • Continue (Cp(U3)=Cb(U3); Cb(U3)Cb(U2)) • So, who ate the mouse?

15. Centering Theory vs. Lappin & Leass • Centering sometimes prefers an antecedent Lappin and Leass (or Hobbs) would consider to have low salience • Always prefers a single pronominalization strategy: prescriptive, assumes discourse coherent • Constraints too simple: grammatical role, recency, repeated mention • Assumes correct syntactic information available as input

16. Hobbs ‘78: Syntax-Based Reference Resolution • Search for antecedent in parse tree of current sentence, then prior sentences in order of recency • For current S, search for NP nodes to the left of a path p from the pronoun up to the first NP or S node (X) above it in L2R, breadth-first • Propose as pronoun’s antecedent any NP you find as long as it has an NP or S node between itself and X • If X is highest node in sentence, search prior sentences, L2R breadth-first, for candidate NPs • O.w., continue searching current tree by going to next S or NP above X before going to prior sentences

17. Evaluation • Centering only now being specified enough to be tested automatically on real data • Specifying the Parameters of Centering Theory: A Corpus-Based Evaluation using Text from Application-Oriented Domains (Poesio et al., ACL 2000) • Walker ‘89 manual comparison of Centering vs. Hobbs ‘78 • Only 281 examples from 3 genres • Assumed correct features given as input to each • Centering 77.6% vs. Hobbs 81.8% • Lappin and Leass’ 86% accuracy on test set from computer training manuals