Learning-based multi-sieve co-reference resolution with knowledge

  • Authors:
  • Lev Ratinov;Dan Roth

  • Affiliations:
  • Google Inc.;University of Illinois at Urbana-Champaign

  • Venue:
  • EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
  • Year:
  • 2012

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Abstract

We explore the interplay of knowledge and structure in co-reference resolution. To inject knowledge, we use a state-of-the-art system which cross-links (or "grounds") expressions in free text to Wikipedia. We explore ways of using the resulting grounding to boost the performance of a state-of-the-art co-reference resolution system. To maximize the utility of the injected knowledge, we deploy a learning-based multi-sieve approach and develop novel entity-based features. Our end system outperforms the state-of-the-art baseline by 2 B3 F1 points on non-transcript portion of the ACE 2004 dataset.