Paper: Maximum Metric Score Training for Coreference Resolution

ACL ID C10-1147
Title Maximum Metric Score Training for Coreference Resolution
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

A large body of prior research on coref- erence resolution recasts the problem as a two-class classification problem. How- ever, standard supervised machine learn- ing algorithms that minimize classifica- tion errors on the training instances do not always lead to maximizing the F-measure of the chosen evaluation metric for coref- erence resolution. In this paper, we pro- pose a novel approach comprising the use of instance weighting and beam search to maximize the evaluation metric score on the training corpus during training. Ex- perimental results show that this approach achieves significant improvement over the state-of-the-art. We report results on stan- dard benchmark corpora (two MUC cor- pora and three ACE corpora), when evalu- ated using the link-based MUC metric and the mention-b...