Does Baum-Welch Re-estimation Help Taggers?

In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which had been tagged by a human annotator to train the model. More recently, Cutting et al. (1992) suggest that training can be achieved with a minimal lexicon and a limited amount of a priori information about probabilities, by using Baum-Welch re-estimation to automatically refine the model. In this paper, I report two experiments designed to determine how much manual training information is needed. The first experiment suggests that initial biasing of either lexical or transition probabilities is essential to achieve a good accuracy. The second experiment reveals that there are three distinct patterns of Baum-Welch reestimation. In two of the patterns, the re-estimation ultimately reduces the accuracy of the tagging rather than improving it. The pattern which is applicable can be predicted from the quality of ...

• Type: Conference
• Year: 1994
• Where: ANLP
• Authors: David Elworthy
• Keywords: ANLP 1994, Grammatical Categories, Hidden Markov Model, Transition Probabilities