A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Improving machine learning approaches to coreference resolution
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Decision Tree Instability and Active Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Active learning for anaphora resolution
HLT '09 Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
Active learning for part-of-speech tagging: accelerating corpus annotation
LAW '07 Proceedings of the Linguistic Annotation Workshop
SemEval-2010 task 1: Coreference resolution in multiple languages
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
Bootstrapping coreference resolution using word associations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
CoNLL-2011 shared task: modeling unrestricted coreference in OntoNotes
CONLL Shared Task '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning: Shared Task
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We present an active learning method for coreference resolution that is novel in three respects. (i) It uses bootstrapped neighborhood pooling, which ensures a class-balanced pool even though gold labels are not available. (ii) It employs neighborhood selection, a selection strategy that ensures coverage of both positive and negative links for selected markables. (iii) It is based on a query-by-committee selection strategy in contrast to earlier uncertainty sampling work. Experiments show that this new method outperforms random sampling in terms of both annotation effort and peak performance.