Myanmar-English Word Translation Disambiguation using Parallel Corpus

Word Sense Disambiguation (WSD) has alwaysbeen a key problem in Natural Language Processing.WSD is defined as the task of finding the correctsense of a word in a specific context. WSD systemscan help to improve the performance of statisticalmachine translation (MT) systems. It is crucial forapplications like Machine Translation andInformation Extraction. Using Naïve Bayesian (NB)classifiers is known as one of the best methods forsupervised approaches for WSD. In this paper, weuse Naïve Bayesian Classifier for solving theambiguity of words in Myanmar language. Thissystem acquires the linguistic knowledge from anannotated corpus and this knowledge is representedin the form of features. As an advantage, the systemcan overcome the problem of translation ambiguityfrom Myanmar to English language translation