Making large-scale support vector machine learning practical
Advances in kernel methods
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
A model-theoretic coreference scoring scheme
MUC6 '95 Proceedings of the 6th conference on Message understanding
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
High-precision identification of discourse new and unique noun phrases
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 2
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Enforcing transitivity in coreference resolution
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
SemEval-2010 task 1: Coreference resolution in multiple languages
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
SemEval-2010 task 1: Coreference resolution in multiple languages
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Journal of Biomedical Informatics
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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Corry is a system for coreference resolution in English. It supports both local (Soon et al. (2001)-style) and global (Integer Linear Programming, Denis and Baldridge (2007)-style) models of coreference. Corry relies on a rich linguistically motivated feature set, which has, however, been manually reduced to 64 features for efficiency reasons. Three runs have been submitted for the SemEval task 1 on Coreference Resolution (Recasens et al., 2010), optimizing Corry's performance for BLANC (Recasens and Hovy, in prep), MUC (Vilain et al., 1995) and CEAF (Luo, 2005). Corry runs have shown the best performance level among all the systems in their track for the corresponding metric.