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Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
2010
Conference on Empirical Methods in Natural Language Processing
We describe a new scalable algorithm for semi-supervised training of conditional random fields (CRF) and its application to partof-speech (POS) tagging. The algorithm uses a similarity graph to encourage similar ngrams to have similar POS tags. We demonstrate the efficacy of our approach on a domain adaptation task, where we assume that we have access to large amounts of unlabeled data from the target domain, but no additional labeled data. The similarity graph is used during training to smooth
dblp:conf/emnlp/SubramanyaPP10
fatcat:imoqrbvfv5bk3nstevm2wvop4e