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Rasekh, Amir Hossein, Sadreddini, Mohammad, Fakhrahmad, Seyed Mostafa. (1393). Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier. , 1(2), 123-132.
Amir Hossein Rasekh; Mohammad Hadi Sadreddini; Seyed Mostafa Fakhrahmad. "Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier". , 1, 2, 1393, 123-132.
Rasekh, Amir Hossein, Sadreddini, Mohammad, Fakhrahmad, Seyed Mostafa. (1393). 'Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier', , 1(2), pp. 123-132.
Rasekh, Amir Hossein, Sadreddini, Mohammad, Fakhrahmad, Seyed Mostafa. Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier. , 1393; 1(2): 123-132.

Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier

Journal of Computing and Security
مقاله 5، دوره 1، شماره 2، تیر 2014، صفحه 123-132    اصل مقاله (538.56 K)
نویسندگان
Amir Hossein Rasekh* 1؛ Mohammad Hadi Sadreddini2؛ Seyed Mostafa Fakhrahmad2
1Shiraz University
2Computer Science and Engineering Department, Shiraz, Iran.
چکیده
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.
کلیدواژه‌ها
Natural Language Processing؛ Word Sense Disambiguation؛ Text Mining
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