Part of Speech Tagging in Thai Language Using Support Vector Machine

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.