Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier
Publish place: Journal of Computing and Security، Vol: 1، Issue: 2
Publish Year: 1393
Type: Journal paper
Language: English
View: 348
This Paper With 10 Page And PDF Format Ready To Download
- Certificate
- I'm the author of the paper
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
Export:
Document National Code:
JR_JCSE-1-2_005
Index date: 29 December 2021
Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier 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.
Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier Keywords:
Word Sense Disambiguation Based on Lexical and Semantic Features Using Naive Bayes Classifier authors
Amir Hossein Rasekh
Shiraz University
Mohammad Sadreddini
Computer Science and Engineering Department, Shiraz, Iran.
Seyed Mostafa Fakhrahmad
Computer Science and Engineering Department, Shiraz, Iran.