Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Tagging English text with a probabilistic model
Computational Linguistics
Unsupervised learning of field segmentation models for information extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Continuation methods for mixing heterogeneous sources
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Joint semi-supervised learning of Hidden Conditional Random Fields and Hidden Markov Models
Pattern Recognition Letters
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This paper explores the use of the homotopy method for training a semi-supervised Hidden Markov Model (HMM) used for sequence labeling. We provide a novel polynomial-time algorithm to trace the local maximum of the likelihood function for HMMs from full weight on the labeled data to full weight on the unlabeled data. We present an experimental analysis of different techniques for choosing the best balance between labeled and unlabeled data based on the characteristics observed along this path. Furthermore, experimental results on the field segmentation task in information extraction show that the Homotopy-based method significantly outperforms EM-based semi-supervised learning, and provides a more accurate alternative to the use of held-out data to pick the best balance for combining labeled and unlabeled data.