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Title:A Fully Bayesian Approach to Unsupervised Part-of-Speech Tagging
Authors: Sharon Goldwater ; T.L. Griffiths
Date: 2007
Publication Title:Proceedings of ACL 2007
Publication Type:Conference Paper Publication Status:Published
Page Nos:744-751
Unsupervised learning of linguistic structure is a difficult problem. A common approach is to define a generative model and maximize the probability of the hidden structure given the observed data. Typically, this is done using maximum-likelihood estimation (MLE) of the model parameters. We show using part-of-speech tagging that a fully Bayesian approach can greatly improve performance. Rather than estimating a single set of parameters, the Bayesian approach integrates over all possible parameter values. This difference ensures that the learned structure will have high probability over a range of possible parameters, and permits the use of priors favoring the sparse distributions that are typical of natural language. Our model has the structure of a standard trigram HMM, yet its accuracy is closer to that of a state-of-the-art discriminative model (Smith and Eisner, 2005), up to 14 percentage points better than MLE. We find improvements both when training from data alone, and using a tagging dictionary.
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Bibtex format
author = { Sharon Goldwater and T.L. Griffiths },
title = {A Fully Bayesian Approach to Unsupervised Part-of-Speech Tagging},
book title = {Proceedings of ACL 2007},
publisher = {ACL},
year = 2007,
pages = {744-751},
url = {},

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