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Coreference Resolution System using Maximum Entropy Classifier

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we present our supervised learning approach to coreference resolution in ConLL corpus. The system relies on a maximum entropy-based classifier for pairs of mentions, and adopts a rich linguisitically motivated feature set, which mostly has been introduced by Soon et al (2001), and experiment with alternaive resolution process, preprocessing tools,and classifiers. We optimize the system's performance for MUC (Vilain et al, 1995), BCUB (Bagga and Baldwin, 1998) and CEAF (Luo, 2005).

Original languageEnglish
Title of host publicationCoNLL 2011 - 15th Conference on Computational Natural Language Learning
Subtitle of host publicationShared Task, Proceedings
EditorsSameer Pradhan
PublisherAssociation for Computational Linguistics (ACL)
Pages127-130
Number of pages4
ISBN (Electronic)9781937284084
StatePublished - 2011
Event15th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2011 - Portland, United States
Duration: 23 Jun 201124 Jun 2011

Publication series

NameCoNLL 2011 - 15th Conference on Computational Natural Language Learning: Shared Task, Proceedings

Conference

Conference15th Conference on Computational Natural Language Learning: Shared Task, CoNLL 2011
Country/TerritoryUnited States
CityPortland
Period23/06/1124/06/11

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