Maximum Metric Score Training for Coreference Resolution

A large body of prior research on coreference resolution recasts the problem as a two-class classification problem. However, standard supervised machine learning algorithms that minimize classification errors on the training instances do not always lead to maximizing the F-measure of the chosen evaluation metric for coreference resolution. In this paper, we propose a novel approach comprising the use of instance weighting and beam search to maximize the evaluation metric score on the training corpus during training. Experimental results show that this approach achieves significant improvement over the state-of-the-art. We report results on standard benchmark corpora (two MUC corpora and three ACE corpora), when evaluated using the link-based MUC metric and the mention-based B-CUBED metric.

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Shanheng Zhao, Hwee Tou Ng
• Keywords: COLING 2010, Computational Linguistics, Coreference Resolution, Evaluation Metric Score, Metric