Supervised Models for Coreference Resolution

Altaf Rahman and Vincent Ng.
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 968-977, 2009.

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

Abstract

Traditional learning-based coreference resolvers operate by training a mention-pair classifier for determining whether two mentions are coreferent or not. Two independent lines of recent research have attempted to improve these mention-pair classifiers, one by learning a mention-ranking model to rank preceding mentions for a given anaphor, and the other by training an entity-mention classifier to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution that combines the strengths of mention rankers and entity-mention models. We additionally show how our cluster-ranking framework naturally allows anaphoricity determination to be learned jointly with coreference resolution. Experimental results on the ACE data sets demonstrate its superior performance to competing approaches.

Software

CherryPicker, a coreference tool that implements the cluster-ranking model described in this paper, is available from this page.

Train-test split

Here are the lists of names of the files from the English portion of the ACE 2005 Multilingual Training Corpus we used for training and testing.

BibTeX entry

@InProceedings{Rahman+Ng:09a,
  author = {Altaf Rahman and Vincent Ng},
  title = {Supervised Models for Coreference Resolution},
  booktitle = {Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing},
  pages = {968--977},
  year = 2009
}