Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models

Amarnag Subramanya, Slav Petrov, Fernando C. N. Pereira
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
more » ... the state posteriors on the target domain. Standard inference can be used at test time. Our approach is able to scale to very large problems and yields significantly improved target domain accuracy.
dblp:conf/emnlp/SubramanyaPP10 fatcat:imoqrbvfv5bk3nstevm2wvop4e