A Bayesian LDA-based Model for Semi-Supervised Part-of-speech Tagging

  • Kristina Toutanova
  • Mark Johnson

In Proceedings of NIPS |

Published by MIT Press

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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 introduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outperforms the best previously proposed model for this task on a standard dataset.