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A New Hybrid Technique for Detection of Plagiarism from Text Documents

  • Research Article-Computer Engineering and Computer Science
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Abstract

Plagiarism occurs when we use the ideas, expressions, work, and words of other authors and do not give them the required attribution. The major contributing factor in plagiarism is the availability of a high amount of data and information on the internet that can be swiftly accessed. The proposed system introduces an extrinsic plagiarism detection approach inspired by cognition because it utilizes semantic knowledge to detect the plagiarized part from the text without human involvement. A lexical database like WordNet assists the computers to perceive the data and information. These days most of the plagiarism detection systems fail to detect highly complex cases of plagiarism. The proposed system uses Dice measure as similarity measure for finding the semantic resemblance between the pair of sentences. It also uses linguistic features like path similarity, depth estimation measure to compute the resemblance between the pair of words and these features are combined by assigning different weights to them. It is capable of identifying cases like restructuring, paraphrasing, verbatim copy, and synonymized plagiarism. It has been evaluated on the PAN-PC-11 corpus. The results obtained from the proposed system signify that it has outperformed other existing systems on PAN-PC-11 in terms of precision, recall, F-measure, and PlagDet score. The proposed system has innovative approach, but the results are somehow close and reasonably better than the existing systems.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Lovepreet Ahuja, Vishal Gupta & Rohit Kumar

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  1. Lovepreet Ahuja
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  2. Vishal Gupta
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  3. Rohit Kumar
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Correspondence to Vishal Gupta.

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Ahuja, L., Gupta, V. & Kumar, R. A New Hybrid Technique for Detection of Plagiarism from Text Documents. Arab J Sci Eng 45, 9939–9952 (2020). https://doi.org/10.1007/s13369-020-04565-9

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  • DOI: https://doi.org/10.1007/s13369-020-04565-9

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