Abstract:
This paper presents Word Sense
Disambiguation for Myanmar Language.
Word Sense Disambiguation (WSD) is an
intermediate but an important step 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) system. In the most
used classifiers, Nearest Neighbor Cosine
(NNC) model has excellent performance, and
Naïve Bayesian (NB) is preferred by
researchers for it is simple and useful. In this
paper, we choose NNC and NB as classifiers
to disambiguate ambiguous Myanmar words
with part-of-speech ‘noun’, ‘verb’ and
‘adjective’. 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. We present a
comparison of two methods in our
experiments.
