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

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. Our model outper- forms the best previously proposed model for this task on a standard dataset.