mlsanityData to 14 Oct 2026

The Struggle with Academic Plagiarism: Approaches based on Semantic Similarity

arXiv 2106.04404 ↗First posted 2 Jun 2021cs.IR cs.LGcited by 1

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Abstract (arXiv)

Academic plagiarism is a serious problem nowadays. Due to the existence of inexhaustible sources of digital information, today it is easier to plagiarize more than ever before. The good thing is that plagiarism detection techniques have improved and are powerful enough to detect attempts of plagiarism in education. We are now witnessing efficient plagiarism detection software in action, such as Turnitin, iThenticate or SafeAssign. In the introduction we explore software that is used within the Croatian academic community for plagiarism detection in universities and/or in scientific journals. The question is: is this enough? Current software has proven to be successful, however the problem of identifying paraphrasing or obfuscation plagiarism remains unresolved. In this paper we present a report of how semantic similarity measures can be used in the plagiarism detection task.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 0 of its 32 references that are in the map. First posted on arXiv 2 Jun 2021.

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