  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 {\it et al.} (1992) suggest that training can be
achieved with a minimal lexicon and a limited amount of {\em 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 re-estimation. 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 the initial model and the similarity between the
tagged training corpus (if any) and the corpus to be tagged. Heuristics for
deciding how to use re-estimation in an effective manner are given. The
conclusions are broadly in agreement with those of Merialdo (1994), but give
greater detail about the contributions of different parts of the model.
