Supervised Models for Coreference Resolution

Traditional learning-based coreference resolvers operate by training a mentionpair 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 mentionranking 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 entitymention 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.

• Type: Journal
• Year: 2009
• Where: EMNLP
• Authors: Md. Altaf ur Rahman, Vincent Ng
• Keywords: EMNLP 2009, Entity-mention Classifier, Learning-based Coreference Resolvers, Mentionpair Classifier, Natural Language Processing