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ANLP
1994

Does Baum-Welch Re-estimation Help Taggers?

13 years 5 months ago
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 ...
David Elworthy
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1994
Where ANLP
Authors David Elworthy
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