A Comparison of Different Measures to Evaluate the Semantic Relatedness of Text and its Application

Vijay .S, Department of Information Technology, Priyadarshini Engineering College, Vaniyambadi (Tamil Nadu), India.
Manuscript received on 18 April 2012 | Revised Manuscript received on 25 April 2012 | Manuscript published on 30 April 2012 | PP: 71-77 | Volume-1 Issue-1, April 2012 | Retrieval Number: B0619052213/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This paper presents a knowledge-based and experiment-based method for measuring the semantic similarity of texts. While there is a large body of previous work focused on finding the semantic similarity of concepts and words, the application of these word oriented methods to text similarity has not been yet explored. Five different proposed measures of similarity or semantic distance in WordNet were experimentally compared by examining their performance in a real-word spelling correction system.
Keywords: Dictionary-Based, Information-Based, Lexical-based, WordNet.

Scope of the Article: Text Mining