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

Kristina Toutanova and Mark Johnson

Abstract

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.

Details

Publication typeInproceedings
Published inIn Proceedings of NIPS
URLhttp://www.mitpress.mit.edu/
PublisherMIT Press
> Publications > A Bayesian LDA-based Model for Semi-Supervised Part-of-speech Tagging