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Using Word Net for Document Clustering: A Detailed Review
Harsha Patil and Dr. R. S. Thakur

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-7, pp.336-343, Dec 2017
DPI :-> 16.10089.JARAS.2017.V4I7.336343.2258



Abstract
Document Clustering is an unsupervised technique for categorized documents in groups on the basis of their similarity. Document clustering techniques are basically very useful to efficiently manage and organize the result of search engine query. Mostly document clustering techniques use Vector Space Model (VSM) to represent any document. VSM based methods generates bag of Words. But VSM based approaches doesn’t consider Semantic relationship among the words. Many researchers are working on semantic aspects of document clustering to improve cluster quality. Since last eight nine years efforts have been seen in applying semantics to document clustering. Many external knowledge bases like Word Net, Wikipedia, and Lucerne etc. are utilized to handle this challenge. The article explains semantic approach in detail and different semantic similarity measures used in algorithms for finding semantic association among words.

Key-Words / Index Term
Document Clustering, Semantic, WordNet, Similarity measures, synonyms.

How to cite this article
Harsha Patil and Dr. R. S. Thakur , “Using Word Net for Document Clustering: A Detailed Review”, Journal for Advance Research in Applied Sciences, 4, Issue-7, pp.336-343, Dec 2017. DPI:16.10089.JARAS.V4.I7.2258