Machine Learning
Machine Learning
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
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
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
Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Machine learning for coreference resolution: from local classification to global ranking
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
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
Using unlabeled data to handle domain-transfer problem of semantic detection
Proceedings of the 2008 ACM symposium on Applied computing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Learning structural SVMs with latent variables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation with latent semantic association for named entity recognition
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
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
Multi-task transfer learning for weakly-supervised relation extraction
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
IEEE Transactions on Knowledge and Data Engineering
Coreference resolution with reconcile
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Narrowing the modeling gap: a cluster-ranking approach to coreference resolution
Journal of Artificial Intelligence Research
Ensemble-based coreference resolution
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
| Hi-index | 0.00 |
We propose an adaptive ensemble method to adapt coreference resolution across domains. This method has three features: (1) it can optimize for any user-specified objective measure; (2) it can make document-specific prediction rather than rely on a fixed base model or a fixed set of base models; (3) it can automatically adjust the active ensemble members during prediction. With simplification, this method can be used in the traditional within-domain case, while still retaining the above features. To the best of our knowledge, this work is the first to both (i) develop a domain adaptation algorithm for the coreference resolution problem and (ii) have the above features as an ensemble method. Empirically, we show the benefits of (i) on the six domains of the ACE 2005 data set in domain adaptation setting, and of (ii) on both the MUC-6 and the ACE 2005 data sets in within-domain setting.