AI-POWERED PLAGIARISM DETECTION: LEVERAGING FORENSIC LINGUISTICS AND NATURAL LANGUAGE PROCESSING
Abstract
Plagiarism of material from the Internet is nothing new to academia and it is particularly rampant. This challenge can range from borrowing a particularly apt phrase without attribution, to paraphrasing someone else’s original idea without citation, to wholesale contract cheating. Plagiarized content can infringe on copyright laws and could incur hefty fines on publishers and authors. Unintentional plagiarism mostly occurs due to inaccurate citation. Most plagiarism checkers ignore this fact. Moreover, plagiarizers are increasingly becoming negatively “smarterâ€. All these necessitate a plagiarism detector that would efficiently handle the challenges. Several plagiarism detectors have been developed but each with its own peculiar limitations. This paper aims at developing an AI-driven plagiarism detector that can crawl the web to index articles and documents, generate similarity score between two local documents, train users on how to properly format in-text citations, identify source code plagiarism and use natural language processing and forensic linguistics to properly analyse plagiarism index.
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