Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41310
Title: Maximum metric score training for co reference resolution
Authors: Zhao, S.
Ng, H.T. 
Issue Date: 2010
Source: Zhao, S.,Ng, H.T. (2010). Maximum metric score training for co reference resolution. Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference 2 : 1308-1316. ScholarBank@NUS Repository.
Abstract: A large body of prior research on co reference 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 co reference 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-basedMUC metric and the mention-based B-CUBED metric.
Source Title: Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/41310
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