Clustering Improvement in Homonym Detection using Concept Based Document Similarity with Conceptual Term Frequency Analysis

Sunil Kumar¹ , Rajendra Gupta²

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.5 , pp.82-87, Oct-2023

Online published on Oct 31, 2023

Copyright © Sunil Kumar, Rajendra Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The homonym words have the same spelling but have different meanings and these words found in almost every language. The homonyms are a source of noise in most text analysis and are difficult to detect. It essentially understands to make correspond to combinations of identifying / difference in parameters like sound, writing, and meaning, according to how the terms are traditionally used; the combination of same sound, same spelling, but distinct meaning is for homonyms. The paper presents a clustering improvement analysis using concept based document similarity method for homonym recognition based on concept based document similarity, which allows a word to be comprehended based on its context. The results show the proposed method shows better performance in clustering improvement and entropy calculation.

Key-Words / Index Term :
Concept based Document Similarity, Homonym Words, Clustering, Entropy

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