Anaphora Resolution: Theory and Practice

More documents

Recommendations

Info

Proposal: Machine Learning for Anaphora Resolution• by applying ML-techniques we are able to determine more variables;• most features are explicit;• it is easy (though time-consuming) to determine the contribution of eachfeature; thus it is possible to decide whether it is necessary to includeexpensive features.
Application: Anaphora Resolution in Written Text(joint work with Stefan Rapp (Sony Research) and Christoph Müller (EML))• ACL ’02: Applying Co-Training to Reference Resolution• EMNLP ’02:ResolutionThe Influence of Minimun Edit Distance on Reference• is it possible to apply Co-Training (a weakly supervised machine learningmeta algorithm) to reference resolution?• German corpus (in the meantime applied to English as well with similarresults);• Co-Training did not work out that well (Ng & Cardie (2003, NAACL) reportbetter results);• however, by doing the Co-Training experiments we got interesting secondaryresults (reported at ACL ’02 and EMNLP ’02).
Page 1 and 2: Anaphora Resolution:Theory and Prac
Page 3 and 4: Anaphora Resolution:orTheory and Pr
Page 5 and 6: A Few Questions• How do insights
Page 7 and 8: Never Look Back: An Alternative to
Page 9 and 10: NLB: The Algorithm1. If a referring
Page 11 and 12: NLB: Results• results obtained by
Page 13 and 14: Application: Anaphora Resolution in
Page 15 and 16: Anaphora Resolution in Spoken Dialo
Page 17 and 18: Types of Anaphora II: Discourse-Dei
Page 19 and 20: Types of Anaphora IV: Inferrable-Ev
Page 21 and 22: Proposal for Pronoun Resolution in
Page 23 and 24: I-Incompatible (*I)x is an anaphor
Page 25 and 26: The Algorithm: Switchboard sw311728
Page 27: Problems with These Approaches• o
Page 31 and 32: NP-level FeaturesDocument level fea
Page 33 and 34: Different Results for Different Typ
Page 35 and 36: Low Amount of Training Data for Pro
Page 37 and 38: Application: Pronoun Resolution for
Page 39 and 40: Data: Corpus• 20 randomly chosen
Page 41 and 42: Data: Distribution of Agreement Fea
Page 43 and 44: Features• NP-level features;• c
Page 45 and 46: Coreference-level Features11. agree
Page 47 and 48: Experimental SetupAnnotated CorpusC
Page 49 and 50: Iterative Procedure(similar to wrap
Page 51 and 52: Results: 3ncorrect found total foun
Page 53 and 54: Results: Combinedcorrect found tota
Page 55 and 56: Conclusions: ML for Pronoun Resolut
Page 57 and 58: Conclusions IIHowever,• I used di
Page 59: Further Information (Papers, Annota

Anaphora Resolution: Theory and Practice

Create successful ePaper yourself

Delete template?

Save as template?