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Automatic Plagiarism Detection using Similarity Analysis
Automatic Plagiarism Detection using Similarity Analysis
2010Journal from gdlhub / 2017-08-14 11:52:32
Oleh : Shanmugasundaram Hariharan, STIKOM Dinamika Bangsa Jambi
Dibuat : 2012-06-23, dengan 1 file
Keyword : Plagiarism detection, similarity measures, information extraction, and text matching.
Subjek : Automatic Plagiarism Detection using Similarity Analysis
Url : http://www.ccis2k.org/iajit/PDF/vol.9,no.4/2796-4.pdf
Sumber pengambilan dokumen : Internet
Plagiarism involves reproducing the existing information in modified format or sometimes the original document as
it is. This is quiet common among students, researchers and academicians. This has made some strong influence on research
community and awareness among academic peoples to prevent such a kind of malpractice. Though there exits some
commercial tools to detect plagiarism, still plagiarism is tricky and quiet challenging task due to abundant information
available online. Commercially existing software adopt methods like paraphrasing, sentence matching or keyword matching.
Such techniques are not too good in identifying the plagiarized contents effectively. However this paper focuses its attention on
identifying some key parameters that would help to identify plagiarism in a better manner. The results seem to be promising
and have further scope in detecting the plagiarism.
Plagiarism involves reproducing the existing information in modified format or sometimes the original document as
it is. This is quiet common among students, researchers and academicians. This has made some strong influence on research
community and awareness among academic peoples to prevent such a kind of malpractice. Though there exits some
commercial tools to detect plagiarism, still plagiarism is tricky and quiet challenging task due to abundant information
available online. Commercially existing software adopt methods like paraphrasing, sentence matching or keyword matching.
Such techniques are not too good in identifying the plagiarized contents effectively. However this paper focuses its attention on
identifying some key parameters that would help to identify plagiarism in a better manner. The results seem to be promising
and have further scope in detecting the plagiarism.
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