C4.5: programs for machine learning
C4.5: programs for machine learning
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to noun phrase coreference resolution
Improving machine learning approaches to noun phrase coreference resolution
Statistical significance of MUC-6 results
MUC6 '95 Proceedings of the 6th conference on Message understanding
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Combining sample selection and error-driven pruning for machine learning of coreference rules
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine learning for coreference resolution: from local classification to global ranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
BART: a modular toolkit for coreference resolution
HLT-Demonstrations '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Demo Session
Practical structured learning techniques for natural language processing
Practical structured learning techniques for natural language processing
Classifier combination techniques applied to coreference resolution
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Using decision trees for conference resolution
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Conundrums in noun phrase coreference resolution: making sense of the state-of-the-art
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Multiobjective simulated annealing based approach for feature selection in anaphora resolution
DAARC'11 Proceedings of the 8th international conference on Anaphora Processing and Applications
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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.