Paper: A fully Bayesian approach to unsupervised part-of-speech tagging

ACL ID P07-1094
Title A fully Bayesian approach to unsupervised part-of-speech tagging
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and max- imize the probability of the hidden struc- ture given the observed data. Typically, this is done using maximum-likelihood es- timation (MLE) of the model parameters. We show using part-of-speech tagging that a fully Bayesian approach can greatly im- prove performance. Rather than estimating a single set of parameters, the Bayesian ap- proach integrates over all possible parame- ter values. This difference ensures that the learned structure will have high probability over a range of possible parameters, and per- mits the use of priors favoring the sparse distributions that are typical of natural lan- guage. Our model has the structure of a standard trigram HMM, ye...