Machine Learning
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Applying system combination to base noun phrase identification
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
Coreference resolution using competition learning approach
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Bootstrapping coreference classifiers with multiple machine learning algorithms
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd 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
Multi-engine machine translation guided by explicit word matching
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Unsupervised models for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Maximum metric score training for coreference resolution
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Ensemble-based coreference resolution
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Domain adaptation for coreference resolution: an adaptive ensemble approach
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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This paper examines the applicability of classifier combination approaches such as bagging and boosting for coreference resolution. To the best of our knowledge, this is the first effort that utilizes such techniques for coreference resolution. In this paper, we provide experimental evidence which indicates that the accuracy of the coreference engine can potentially be increased by use of bagging and boosting methods, without any additional features or training data. We implement and evaluate combination techniques at the mention, entity and document level, and also address issues like entity alignment, that are specific to coreference resolution.