Improved estimation for unsupervised part-of-speech tagging
- Qin Iris Wang, Department of Computing Science, University of Alberta
- Dale Schuurmans, AICML
We demonstrate that a simple hidden Markov model can achieve state of the art performance in unsupervised part-of-speech tagging, by improving aspects of standard Baum- Welch (EM) estimation. One improvement uses word similarities to smooth the lexical tag->word probability estimates, which avoids over-fitting the lexical model. Another improvement constrains the model to preserve a specified marginal distribution over the hidden tags, which avoids over-fitting the tag->tag transition model. Although using more contextual information than an HMM remains desirable, improving basic estimation still leads to significant improvements and remains a prerequisite for training more complex models.
Citation
Q. Wang, D. Schuurmans. "Improved estimation for unsupervised part-of-speech tagging". IEEE, January 2005.| Keywords: | machine learning |
| Category: | In Conference |
BibTeX
@incollection{Wang+Schuurmans:IEEE05,
author = {Qin Iris Wang and Dale Schuurmans},
title = {Improved estimation for unsupervised part-of-speech tagging},
booktitle = {},
year = 2005,
}Last Updated: March 13, 2007Submitted by AICML Admin Assistant
