Paper: Machine Learning For Coreference Resolution: From Local Classification To Global Ranking

ACL ID P05-1020
Title Machine Learning For Coreference Resolution: From Local Classification To Global Ranking
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors
  • Vincent Ng (University of Texas at Dallas, Richardson TX)

In this paper, we view coreference reso- lution as a problem of ranking candidate partitions generated by different coref- erence systems. We propose a set of partition-based features to learn a rank- ing model for distinguishing good and bad partitions. Our approach compares fa- vorably to two state-of-the-art coreference systems when evaluated on three standard coreference data sets.