Paper: A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers

ACL ID D08-1036
Title A comparison of Bayesian estimators for unsupervised Hidden Markov Model POS taggers
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

There is growing interest in applying Bayesian techniques to NLP problems. There are a number of different estimators for Bayesian models, and it is useful to know what kinds of tasks each does well on. This paper compares a variety of different Bayesian estimators for Hidden Markov Model POS taggers with var- ious numbers of hidden states on data sets of different sizes. Recent papers have given con- tradictory results when comparing Bayesian estimators to Expectation Maximization (EM) for unsupervised HMM POS tagging, and we show that the difference in reported results is largely due to differences in the size of the training data and the number of states in the HMM. We invesigate a variety of samplers for HMMs, including some that these earlier pa- pers did not study. We find that all o...