A Bayesian LDA-based model for semi-supervised part-of-speech tagging

Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)

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Kristina Toutanova, Mark Johnson


We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that words’ distributions over tags, p(t|w), are sparse. In addition we in- troduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outper- forms the best previously proposed model for this task on a standard dataset.