Improving machine learning approaches to coreference resolution

  • Authors:
  • Vincent Ng;Claire Cardie

  • Affiliations:
  • Cornell University, Ithaca, NY;Cornell University, Ithaca, NY

  • Venue:
  • ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
  • Year:
  • 2002

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Abstract

We present a noun phrase coreference system that extends the work of Soon et al. (2001) and, to our knowledge, produces the best results to date on the MUC-6 and MUC-7 coreference resolution data sets --- F-measures of 70.4 and 63.4, respectively. Improvements arise from two sources: extra-linguistic changes to the learning framework and a large-scale expansion of the feature set to include more sophisticated linguistic knowledge.