Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Detection of agreement vs. disagreement in meetings: training with unlabeled data
NAACL-Short '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: companion volume of the Proceedings of HLT-NAACL 2003--short papers - Volume 2
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Multimodal subjectivity analysis of multiparty conversation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Agreement detection in multiparty conversation
Proceedings of the 2009 international conference on Multimodal interfaces
Detection of agreement and disagreement in broadcast conversations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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Several semi-supervised learning methods have been proposed to leverage unlabeled data, but imbalanced class distributions in the data set can hurt the performance of most algorithms. In this paper, we adapt the new approach of contrast classifiers for semi-supervised learning. This enables us to exploit large amounts of unlabeled data with a skewed distribution. In experiments on a speech act (agreement/disagreement) classification problem, we achieve better results than other semi-supervised methods. We also obtain performance comparable to the best results reported so far on this task and outperform systems with equivalent feature sets.