Machine Learning for Coreference Resolution: From Local Classification to Global Ranking

Vincent Ng.
Proceedings of the 43rd Annual Meeting of the Asssociation for Computational Linguistics (ACL), pp. 157-164, 2005.

Click here for the PostScript or PDF version. The talk slides are available here.

Abstract

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

BibTeX entry

@InProceedings{Ng:05a,
  author = {Vincent Ng},
  title = {Machine Learning for Coreference Resolution: From Local Classification to Global Ranking},
  booktitle = {Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics},
  pages = {157--164},
  year = 2005
}