A state-of-the-art mention-pair model for coreference resolution

Olga Uryupina, Alessandro Moschitti

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

2 Citations (Scopus)

Abstract

Most recent studies on coreference resolution advocate accurate yet relatively complex models, relying on, for example, entitymention or graph-based representations. As it has been convincingly demonstrated at the recent CoNLL 2012 shared task, such algorithms considerably outperform popular basic approaches, in particular mention-pair models. This study advocates a novel approach that keeps the simplicity of a mention-pair framework, while showing state-of-the-art results. Apart from being very efficient and straightforward to implement, our model facilitates experimental work on the pairwise classifier, in particular on feature engineering. The proposed model achieves the performance level of up to 61.82% (MELA F, v4 scorer) on the CoNLL test data, on par with complex state-of-the-art systems.

Original languageEnglish
Title of host publicationProceedings of the 4th Joint Conference on Lexical and Computational Semantics, *SEM 2015
PublisherAssociation for Computational Linguistics (ACL)
Pages289-298
Number of pages10
ISBN (Electronic)9781941643396
Publication statusPublished - 1 Jan 2015
Event4th Joint Conference on Lexical and Computational Semantics, *SEM 2015 - Denver, United States
Duration: 4 Jun 20155 Jun 2015

Other

Other4th Joint Conference on Lexical and Computational Semantics, *SEM 2015
CountryUnited States
CityDenver
Period4/6/155/6/15

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Uryupina, O., & Moschitti, A. (2015). A state-of-the-art mention-pair model for coreference resolution. In Proceedings of the 4th Joint Conference on Lexical and Computational Semantics, *SEM 2015 (pp. 289-298). Association for Computational Linguistics (ACL).