Mohamed El Bachir Menai

Work place: Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

E-mail: menai@ksu.edu.sa

Website:

Research Interests: Computational Learning Theory, Natural Language Processing, Computer Architecture and Organization, Computing Platform

Biography

Mohamed El Bachir Menai received a Ph.D. degree in computer science from Mentouri University of Constantine, Algeria, and University of Paris VIII, France, in 2005. He received also a “Habilitation universitaire” in computer science from Mentouri University of Constantine, in 2007 (it is the highest academic qualification in Algeria and France). He is currently associate professor in the department of computer science at King Saud University. His main interests include satisfiability problems, evolutionary computing, machine learning, and natural language processing.

Author Articles
Detection of Plagiarism in Arabic Documents

By Mohamed El Bachir Menai

DOI: https://doi.org/10.5815/ijitcs.2012.10.10, Pub. Date: 8 Sep. 2012

Many language-sensitive tools for detecting plagiarism in natural language documents have been developed, particularly for English. Language-independent tools exist as well, but are considered restrictive as they usually do not take into account specific language features. Detecting plagiarism in Arabic documents is particularly a challenging task because of the complex linguistic structure of Arabic. In this paper, we present a plagiarism detection tool for comparison of Arabic documents to identify potential similarities. The tool is based on a new comparison algorithm that uses heuristics to compare suspect documents at different hierarchical levels to avoid unnecessary comparisons. We evaluate its performance in terms of precision and recall on a large data set of Arabic documents, and show its capability in identifying direct and sophisticated copying, such as sentence reordering and synonym substitution. We also demonstrate its advantages over other plagiarism detection tools, including Turnitin, the well-known language-independent tool.

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