First-Order Probabilistic Models for Coreference Resolution

  title={First-Order Probabilistic Models for Coreference Resolution},
  author={Aron Culotta and Michael L. Wick and Andrew McCallum},
Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases. In this paper, we propose a machine learning method that enables features over sets of noun phrases, resulting in a first-order probabilistic model for coreference. We outline a set of approximations that make this approach practical, and apply our method to the ACE coreference dataset, achieving a 45% error reduction over a comparable method that only considers features of pairs of noun… CONTINUE READING
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