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

ACL ID D10-1017
Title Efficient Graph-Based Semi-Supervised Learning of Structured Tagging Models
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010
Authors

We describe a new scalable algorithm for semi-supervised training of conditional ran- dom fields (CRF) and its application to part- of-speech (POS) tagging. The algorithm uses a similarity graph to encourage similar n- grams to have similar POS tags. We demon- strate the efficacy of our approach on a do- main 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 dur- ing training to smooth 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.