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Computer Science and Engineering Department, Shiraz, Iran.
Abstract
Machine translation is considered as a branch of machine intelligence with about fifty years background. Ambiguity of language is the most problematic issue in machine translation systems, which may lead to unclear or wrong translation. One of the problems involved in natural language processing is the semantic and structural ambiguity of the words. The objective of this paper to focused on the word sense disambiguation. In here, the existing algorithms for word sense disambiguation are evaluated and a method which is proposed based on the concept, structure and meaning of the words. The experimental results are promising and indicate that this proposed approach significantly outperform its counterparts in terms of disambiguation accuracy.
Rasekh, A. H., Sadreddini, M., & Fakhrahmad, S. M. (2014). Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier. Journal of Computing and Security, 1(2), 123-132.
MLA
Amir Hossein Rasekh; Mohammad Hadi Sadreddini; Seyed Mostafa Fakhrahmad. "Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier". Journal of Computing and Security, 1, 2, 2014, 123-132.
HARVARD
Rasekh, A. H., Sadreddini, M., Fakhrahmad, S. M. (2014). 'Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier', Journal of Computing and Security, 1(2), pp. 123-132.
VANCOUVER
Rasekh, A. H., Sadreddini, M., Fakhrahmad, S. M. Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier. Journal of Computing and Security, 2014; 1(2): 123-132.
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