DocumentCode
1929951
Title
A predominant statistical approach to identify semantic similarity of textual documents
Author
Vigneshvaran, P. ; Jayabalan, E. ; Vijaya, K.
Author_Institution
Dept. of Comput. Sci., Gov. Arts Coll. (Autonomous), Salem, India
fYear
2013
fDate
21-22 Feb. 2013
Firstpage
496
Lastpage
499
Abstract
Semantic similarity is the processes of identifying similar words. It relates to computing the similarity between documents which are not lexicographically similar. This paper proposed an empirical method to estimate the semantic similarity using HBase. Specifically this paper defines various word co-occurrence in the document measured and its synonyms are also identified using WordNet. By using the statistical approaches such as MSE and MSD, similarity has been measured. This research focuses on evaluating the similarity between the key document and source documents in the document corpus. In this paper, the developed predominant tool using statistical approach has been tested by checking the similarity of the assignments submitted by the students to check the integrity of a student. This tool may also be used to identify Plagiarism of documents and to eliminate duplicates in a text repository.
Keywords
statistical analysis; text analysis; HBase; MSD; MSE; WordNet; document corpus; document similarity; empirical method; plagiarism; predominant statistical approach; semantic similarity identification; similar word identification; source document; student assignment; student integrity; text repository; textual document; word cooccurrence; Computational modeling; Context; Databases; Informatics; Pattern recognition; Semantics; Vectors; HBase; Key Document; MSD (Mean Square Deviation); MSE (Mean Square Error); Semantic Similarity; Source Document; document corpus;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, Informatics and Mobile Engineering (PRIME), 2013 International Conference on
Conference_Location
Salem
Print_ISBN
978-1-4673-5843-9
Type
conf
DOI
10.1109/ICPRIME.2013.6496721
Filename
6496721
Link To Document