Ambiguous Myanmar Word Disambiguation for Myanmar-English Machine Translation

Abstract

Word Sense Disambiguation (WSD) has always been a key problem in Natural Language Processing. WSD is defined as the task of finding the correct sense of a word in a specific context. There is not any cited work for resolving ambiguity of words in Myanmar language. Using Naïve Bayesian (NB) classifiers is known as one of the best methods for supervised approaches for WSD. In this paper, we use Naïve Bayesian Classifier to disambiguate ambiguous Myanmar words with part-of-speech ‘noun’ and ‘verb’, which uses topical feature that represent cooccurring words in bag-of-word feature. The system also uses Myanmar-English Parallel Corpus as training data. The WSD module developed here will be used as a complement to improve Myanmar-English machine translation system. As an advantage, the system can improve the accuracy of Myanmar to English language translation.