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.
