Paper: Supervised Models for Coreference Resolution

ACL ID D09-1101
Title Supervised Models for Coreference Resolution
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009

Traditional learning-based coreference re- solvers operate by training a mention- pair classifier for determining whether two mentions are coreferent or not. Two in- dependent lines of recent research have attempted to improve these mention-pair classifiers, one by learning a mention- ranking model to rank preceding men- tions for a given anaphor, and the other by training an entity-mention classifier to determine whether a preceding clus- ter 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 natu- rally allows discourse-new entity detection to be learned jointly with coreference res- olution. Experimental result...