  The elastic-input neuro tagger and hybrid tagger, combined with a neural
network and Brill's error-driven learning, have already been proposed for the
purpose of constructing a practical tagger using as little training data as
possible. When a small Thai corpus is used for training, these taggers have
tagging accuracies of 94.4% and 95.5% (accounting only for the ambiguous words
in terms of the part of speech), respectively. In this study, in order to
construct more accurate taggers we developed new tagging methods using three
machine learning methods: the decision-list, maximum entropy, and support
vector machine methods. We then performed tagging experiments by using these
methods. Our results showed that the support vector machine method has the best
precision (96.1%), and that it is capable of improving the accuracy of tagging
in the Thai language. Finally, we theoretically examined all these methods and
discussed how the improvements were achived.
