Myanmar-English Word Translation Disambiguation using Parallel Corpus

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. WSD systems can help to improve the performance of statistical machine translation (MT) systems. It is crucial for applications like Machine Translation and Information Extraction. 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 for solving the ambiguity of words in Myanmar language. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. As an advantage, the system can overcome the problem of translation ambiguity from Myanmar to English language translation.