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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 9    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 9, September 2011
A Word Sense Disambiguation System Using Naïve Bayesian Algorithm for Myanmar Language
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Nyein Thwet Thwet Aung, Khin Mar Soe, Ni Lar Thein
Myanmar-English machine translation, Myanmar-English parallel corpus, Naïve Bayes Classifier, Natural Language Processing, supervised approach, unsupervised approach, Word Sense Disambiguation
Natural Language Processing has been developed to allow human-machine communication to take place in a natural-language. 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. Several methodological issues come up with the context of WSD. These are supervised and unsupervised WSD approaches. Supervised WSD approaches have obtained better results than unsupervised WSD approaches. 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 method 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'. 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.
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