Improved estimation for unsupervised part-of-speech tagging
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 /spl rarr/ 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 /spl rarr/ 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.