Semi-Supervised Conditional Random Fields for Improved Sequence Segmentation and Labeling

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The note that CRFs have proved to be particularly useful for sequence segmentation and labeling tasks, since, as conditional models of the labels given inputs, they relax the independence assumptions made by traditional generative models like hidden Markov models. In this paper, we propose a new semi-supervised training method for conditional random fields (CRFs) that incorporates both labeled and unlabeled sequence data to estimate a discriminative structured predictor.