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Unsupervised learning of contextual role knowledge for coreference resolution

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a coreference resolver called BABAR that uses contextual role knowledge to evaluate possible antecedents for an anaphor. BABAR uses information extraction patterns to identify contextual roles and creates four contextual role knowledge sources using unsupervised learning. These knowledge sources determine whether the contexts surrounding an anaphor and antecedent are compatible. BABAR applies a Dempster-Shafer probabilistic model to make resolutions based on evidence from the contextual role knowledge sources as well as general knowledge sources. Experiments in two domains showed that the contextual role knowledge improved coreference performance, especially on pronouns.

Original languageEnglish (US)
Title of host publicationHLT-NAACL 2004 - Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages297-304
Number of pages8
ISBN (Electronic)193243223X, 9781932432237
StatePublished - 2004
Externally publishedYes
Event2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2004 - Boston, United States
Duration: May 2 2004May 7 2004

Publication series

NameHLT-NAACL 2004 - Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference

Conference

Conference2004 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, HLT-NAACL 2004
Country/TerritoryUnited States
CityBoston
Period5/2/045/7/04

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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