TY - GEN

T1 - A fully Bayesian approach to unsupervised part-of-speech tagging

AU - Goldwater, Sharon

AU - Griffiths, Thomas L.

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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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M3 - Conference contribution

AN - SCOPUS:84860525845

SN - 9781932432862

T3 - ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

SP - 744

EP - 751

BT - ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

T2 - 45th Annual Meeting of the Association for Computational Linguistics, ACL 2007

Y2 - 23 June 2007 through 30 June 2007

ER -