• 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