Plagiarism is a breach of copyright in the academic world. It has become a serious issue after explosion of digital information as copying has become easier due to huge amount of source but detection has become more difficult. Plagiarism can be of two types: source code plagiarism refers to copying the code from proprietary software and text plagiarism which deals with copying from others text and pretending it as own. There are several tools to detect both type of plagiarism. In this paper we have concentrated mainly on text plagiarism discussed about algorithms used in software available like Turnitin, iThenticate or SafeAssign to detect plagiarism and how NLP techniques and parallel processing can improve them. Mostly all software determine a similarity score for each pair of document and use SCAM (Standard Copy Analysis Mechanism) algorithm to calculate relative measure of overlapping during comparison of common set of words. We have tried to establish how semantic similarity can improve TRUE POSITIVE and TRUE NEGATIVE detection and reduce FALSE POSITIVE and FALSE NEGATIVE detection in our work.

Plagiarism, Semantic Similarity, Semantic Search, Turnitin, WordNet, Ontologies, Natural Language Processing

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