Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Entity-based cross-document coreferencing using the Vector Space Model
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Evaluating automated and manual acquisition of anaphora resolution strategies
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Weakly supervised natural language learning without redundant views
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
A mention-synchronous coreference resolution algorithm based on the Bell tree
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A machine learning approach to German pronoun resolution
ACLstudent '04 Proceedings of the ACL 2004 workshop on Student research
Machine learning for coreference resolution: from local classification to global ranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Optimizing to arbitrary NLP metrics using ensemble selection
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Understanding the value of features for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Specialized models and ranking for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in 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
Coreference resolution in a modular, entity-centered model
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Narrowing the modeling gap: a cluster-ranking approach to coreference resolution
Journal of Artificial Intelligence Research
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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We investigate new methods for creating and applying ensembles for coreference resolution. While existing ensembles for coreference resolution are typically created using different learning algorithms, clustering algorithms or training sets, we harness recent advances in coreference modeling and propose to create our ensemble from a variety of supervised coreference models. However, the presence of pairwise and non-pairwise coreference models in our ensemble presents a challenge to its application: it is not immediately clear how to combine the coreference decisions made by these models. We investigate different methods for applying a model-heterogeneous ensemble for coreference resolution. Empirical results on the ACE data sets demonstrate the promise of ensemble approaches: all ensemble-based systems significantly outperform the best member of the ensemble.