Unrestricted coreference resolution via global hypergraph partitioning

  • Authors:
  • Jie Cai;Éva Mújdricza-Maydt;Michael Strube

  • Affiliations:
  • Natural Language Processing Group, Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, Germany;Natural Language Processing Group, Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, Germany;Natural Language Processing Group, Heidelberg Institute for Theoretical Studies gGmbH, Heidelberg, Germany

  • Venue:
  • CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
  • Year:
  • 2011

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Abstract

We present our end-to-end coreference resolution system, COPA, which implements a global decision via hypergraph partitioning. In constrast to almost all previous approaches, we do not rely on separate classification and clustering steps, but perform coreference resolution globally in one step. COPA represents each document as a hypergraph and partitions it with a spectral clustering algorithm. Various types of relational features can be easily incorporated in this framwork. COPA has participated in the open setting of the CoNLL shared task on modeling unrestricted coreference.