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. 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.

nips2007_0964.pdf
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In  In Proceedings of NIPS

Publisher  MIT Press
All copyrights reserved by MIT Press 2007.

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TypeInproceedings
URLhttp://www.mitpress.mit.edu/
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