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All Journal Bulletin of Electrical Engineering and Informatics
Dana Halabi
The World Islamic Sciences and Education University
Electrical & Electronics Engineering

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Evaluating lexical feature extraction for plagiarism detection in Arabic documents Marwah Alian; Dana Halabi; Hadeel Rida Alshboul
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.11767

Abstract

Plagiarism detection is the task of determining whether a document contains parts from other documents by employing different styles of plagiarism, such as copying certain parts and reordering or replacing words with synonyms, without citing the original text owner. This task is important in many applications, and there are two primary types of plagiarism detection methods: external and intrinsic. Plagiarism detection in Arabic documents is challenging because of Arabic’s rich morphological features, lexical variation, and syntactic complexity, which limit the effectiveness of some detection approaches. To address these challenges, this study introduces an external plagiarism detection framework built on an artificial neural network (ANN) model and a lexical feature extraction framework adapted to the linguistic features of Arabic. The proposed framework is evaluated using ExAraPlagDet-2015 benchmark, where a baseline model using support vector machine (SVM) is introduced for comparison. Experimental results demonstrate notable improvements in plagiarism detection performance of the proposed framework compared with SVM and other baseline methods. The proposed framework provides a precision value of 92% and an F-score value of 96%, verifying its effectiveness for Arabic plagiarism detection.
Co-Authors Hadeel Rida Alshboul Marwah Alian