DOC PREVIEW
Columbia COMS W4705 - Algorithms for Reference Resolution

This preview shows page 1-2-22-23 out of 23 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 23 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 23 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 23 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 23 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 23 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

CS 4705Anaphora resolutionReview: What Factors Affect Reference Resolution?Slide 4Reference ResolutionIssuesThree AlgorithmsLappin & Leass ‘94Saliency Factor WeightsSlide 10Slide 11Slide 12A Different Aproach: Centering TheoryCentering theory: MotivationCentering theory: DefinitionsMore DefinitionsTransitions from Un to Un+1RulesExampleSlide 20Centering Theory vs. Lappin & LeassEvaluationRule-based vs. Statistical ApproachesCS 4705CS 4705Algorithms for Reference ResolutionAnaphora resolution•Finding in a text all the referring expressions that have one and the same denotation–Pronominal anaphora resolution–Anaphora resolution between named entities–Full noun phrase anaphora resolutionReview: What Factors Affect Reference Resolution? •Lexical factors–Reference type: Inferability, discontinuous set, generics, one anaphora, pronouns,…•Discourse factors:–Recency–Focus/topic structure, digression–Repeated mention•Syntactic factors:–Agreement: gender, number, person, case–Parallel construction–Grammatical role–Selectional restrictions•Semantic/lexical factors–Verb semantics, thematic role •Pragmatic factorsReference Resolution•Given these types of constraints, can we construct an algorithm that will apply them such that we can identify the correct referents of anaphors and other referring expressions?Issues•Which constraints/features can/should we make use of?•How should we order them? I.e. which override which?•What should be stored in our discourse model? I.e., what types of information do we need to keep track of?•How to evaluate?Three Algorithms•Lappin & Leas ‘94: weighting via recency and syntactic preferences•Hobbs ‘78: syntax tree-based referential search•Centering (Grosz, Joshi, Weinstein, ‘95 and various): discourse-based searchLappin & 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-reflexivesSaliency Factor Weights•Sentence recency (in current sentence?) 100•Subject emphasis (is it the subject?) 80•Existential emphasis (existential prednom?) 70•Accusative emphasis (is it the dir obj?) 50•Indirect object/oblique comp emphasis 40•Non-adverbial emphasis (not in PP,) 50•Head noun emphasis (is head noun) 80•Implicit ordering of arguments:subj/exist pred/obj/indobj-oblique/dem.advPPOn the sofa, the cat was eating bonbons.sofa: 100+80=180cat: 100+80+50+80=310bonbons: 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 co-referential items (exceptions, e.g. multiple references in same sentence)The bonbons were clearly very tasty.sofa: 180/2=90cat: 310/2=155bonbons: 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/syntaxThey were a gift from an unknown admirer.sofa: 90/2=45cat: 155/2=77.5bonbons: 450/2=225 (+35) = 260….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 stringA Different Aproach: Centering Theory•(Grosz et al 1995) examines interactions between local coherence and the choice of referring expressions–A pretty woman entered the restaurant. She sat at the table next to mine…–A woman entered the restaurant. They like ice cream.Centering theory: Motivation•(Grosz et al 1995) examine interactions between local coherence and the choice of referring expressions–Pronouns and definite descriptions are not equivalent with respect to their effect on coherence– Different inference demands on the hearer/reader.Centering theory: Definitions•The centers of an utterance are discourse entities serving to link the utterance to other utterances–Forward looking centers: a ranked list–A backward looking center: the entity currently ‘in focus’ or salient•Centers are semantic objects, not words, phrases, or syntactic forms but–They are realized by such in an utterance –Their realization can give us clues about their likely salienceMore Definitions•More on discourse centers and utterances –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)•Cf may be ordered subj<exist. Prednom<obj<indobj-oblique<dem. advPP (Brennan et al)•Cp(Un): preferred (highest ranked) center of Cf(Un)Transitions from Un to Un+1Rules•If any element of Cf(Un) is pronominalized in Un+1, then Cb(Un+1) must also be•Preference: Continue > Retain > Smooth-Shift > Rough-Shift•Algorithm–Generate Cb and Cf assignments for all possible reference assignments–Filter by constraints (syntactic coreference, selectional restrictions,…)–Rank by preference among transition orderingsExampleU1:George gave Harry a cookie. U2:He baked the cookie Thursday. U3: He ate the cookie all up.•One–Cf(U1): {George,cookie,Harry}–Cp(U1): George–Cb(U1): undefined•Two–Cf(U2): {George,cookie,Thursday} –Cp(U2): George–Cb(U2): George–Continue (Cp(U2)=Cb(U2); Cb(U1) undefined•Three–Cf(U3): {George?,cookie}–Cp(U3): George?–Cb(U3): George?–Continue (Cp(U3)=Cb(U3); Cb(U3)= Cb(U2)•Or, Three–Cf(U3): {Harry?,cookie}–Cp(U3): Harry?–Cb(U3): Harry?–Smooth-Shift (Cp(U3)=Cb(U3); Cb(U3)  Cb(U2)The winner is…..George!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


View Full Document

Columbia COMS W4705 - Algorithms for Reference Resolution

Download Algorithms for Reference Resolution
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Algorithms for Reference Resolution and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Algorithms for Reference Resolution 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?