C4.5: programs for machine learning
C4.5: programs for machine learning
The weighted majority algorithm
Information and Computation
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Selective sampling for example-based word sense disambiguation
Computational Linguistics
Word sense disambiguation using optimised combinations of knowledge sources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A semi-supervised feature clustering algorithm with application to word sense disambiguation
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Word sense disambiguation by semi-supervised learning
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
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Most corpus-based approaches to natural language processing suffer from lack of training data. This is because acquiring a large number of labeled data is expensive. This paper describes a learning method that exploits unlabeled data to tackle data sparseness problem. The method uses committee learning to predict the labels of unlabeled data that augment the existing training data. Our experiments on word sense disambiguation show that predictive accuracy is significantly improved by using additional unlabeled data.