Citation

Sharmila, P. and Sonai Muthu Anbananthen, Kalaiarasi and Nithyakala, G. and Balasubramaniam, Baarathi and Deisy, C. (2024) FTLM: A Fuzzy TOPSIS Language Modeling Approach for Plagiarism Severity Assessment. IEEE Access. p. 1. ISSN 2169-3536

Text FTLM_ A Fuzzy TOPSIS Language Modeling Approach for Plagiarism Severity Assessment.pdf - Published Version Restricted to Repository staff only Download (918kB)

Abstract

Detecting plagiarism poses a significant challenge for academic institutions, research centers, and content-centric organizations, especially in cases involving subtle paraphrasing and content manipulation where conventional methods often prove inadequate. Our paper proposes FTLM (Fuzzy TOPSIS Language Modeling), a novel method for detecting plagiarism within decision science. FTLM integrates language models with fuzzy sorting techniques to assess plagiarism severity by evaluating the similarity of potential solutions to a reference. The method involves two stages: leveraging language modeling to define criteria and alternatives and implementing enhanced fuzzy TOPSIS. Word usage patterns, grammatical structures, and semantic coherence represent fuzzy membership functions. Moreover, pre-trained language models enhance semantic similarity analysis. This approach highlights the benefits of combining fuzzy logic’s tolerance for imprecision with the semantic evaluation capabilities of advanced language models, thereby offering a comprehensive and contextually aware method for analyzing plagiarism severity. The experimental results on the benchmark dataset demonstrate effective features that enhance performance on the user-defined severity ranking order.

Item Type:	Article
Uncontrolled Keywords:	Plagiarism
Subjects:	P Language and Literature > PA Classical philology
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Sep 2024 08:18
Last Modified:	02 Sep 2024 08:18
URII:	http://shdl.mmu.edu.my/id/eprint/12927

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