Coreference resolution using competition learning approach

  • Authors:

  • Xiaofeng Yang;Guodong Zhou;Jian Su;Chew Lim Tan

  • Affiliations:

  • Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;Institute for Infocomm Research, Singapore;National University of Singapore, Singapore

  • Venue:
  • ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
  • Year:
  • 2003

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Abstract

In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the single-candidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the single-candidate model.