Using Natural Language Processing techniques and fuzzy-semantic similarity for automatic external plagiarism detection

Plagiarism is one of the most serious crimes in academia and research fields. In this modern era, where access to information has become much easier, the act of plagiarism is rapidly increasing. This paper aligns on external plagiarism detection method, where the source collection of documents is available against which the suspicious documents are compared. Primary focus is to detect intelligent plagiarism cases where semantics and linguistic variations play an important role. The paper explores the different preprocessing methods based on Natural Language Processing (NLP) techniques. It further explores fuzzy-semantic similarity measures for document comparisons. The system is finally evaluated using PAN 2012
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data set and performances of different methods are compared.