Hidden Markov Model with Rule Based Approach for Part of Speech Tagging of Myanmar Language

Part-of-Speech (POS) Tagging is the process of assigning the words with their categories that best suits the definition of the word as well as the context of the sentence in which it is used. There are different approaches to the problem of PUS Tagging. In this paper, we use two approaches (rule based and Hidden Markov Model with rule based approach), and compare the performance of these techniques for Tagging using Myanmar language. These approaches use supervised PUS Tagging that requires a large amount of annotated training corpus to tag properly. At this initial stage of POS Tagging for Myanmar Language, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resources. By experiments, the best configuration is investigated using HMM with rule based approach and the accuracy is 97.56%. Therefore, this approach has better performance than rule based approach.