Identifying anaphoric and non-anaphoric noun phrases to improve coreference resolution

  • Authors:
  • Vincent Ng;Claire Cardie

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

  • Venue:
  • COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
  • Year:
  • 2002

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Abstract

We present a supervised learning approach to identification of anaphoric and non-anaphoric noun phrases and show how such information can be incorporated into a coreference resolution system. The resulting system outperforms the best MUC-6 and MUC-7 coreference resolution systems on the corresponding MUC coreference data sets, obtaining F-measures of 66.2 and 64.0, respectively.