<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Measuring the amount of shared information between two documents is a key to address a number of Natural Language Processing (NLP) challenges such as Information Retrieval (IR), Semantic Textual Similarity (STS), Sentiment Analysis (SA) and Plagiarism Detection (PD). In this paper, we report a plagiarism detection system based on two layers of assessment: 1) Fingerprinting which simply compares the documents fingerprints to detect the verbatim reproduction; 2) Word embedding which uses the semantic and syntactic properties of words to detect much more complicated reproductions. Moreover, Word Alignment (WA), Inverse Document Frequency (IDF) and Part-of-Speech (POS) weighting are applied on the examined documents to support the identification of words that are most descriptive in each textual unit. In the present work, we focused on Arabic documents and we evaluated the performance of the system on a data-set of holding three types of plagiarism: 1) Simple reproduction (copy and paste); 2) Word and phrase shuffling; 3) Intelligent plagiarism including synonym substitution, diacritics insertion and paraphrasing. The results show a recall of 88% and a precision of 86%. Compared to the results obtained by the systems participating in the Arabic Plagiarism Detection Shared Task 2015, our system outperforms all of them with a plagiarism detection score (Plagdet) of 83%.</p></abstract>

Measuring the amount of shared information between two documents is a key to address a number of Natural Language Processing (NLP) challenges such as Information Retrieval (IR), Semantic Textual Similarity (STS), Sentiment Analysis (SA) and Plagiarism Detection (PD). In this paper, we report a plagiarism detection system based on two layers of assessment: 1) Fingerprinting which simply compares the documents fingerprints to detect the verbatim reproduction; 2) Word embedding which uses the semantic and syntactic properties of words to detect much more complicated reproductions. Moreover, Word Alignment (WA), Inverse Document Frequency (IDF) and Part-of-Speech (POS) weighting are applied on the examined documents to support the identification of words that are most descriptive in each textual unit. In the present work, we focused on Arabic documents and we evaluated the performance of the system on a data-set of holding three types of plagiarism: 1) Simple reproduction (copy and paste); 2) Word and phrase shuffling; 3) Intelligent plagiarism including synonym substitution, diacritics insertion and paraphrasing. The results show a recall of 88% and a precision of 86%. Compared to the results obtained by the systems participating in the Arabic Plagiarism Detection Shared Task 2015, our system outperforms all of them with a plagiarism detection score (Plagdet) of 83%.

2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents

Laboratoire d’Informatique et de Mathématique LIM

Al-Imam Mohammad Ibn Saud Islamic University

Laboratoire d’Informatique de Grenoble LIG-GETALP

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents"}

Measuring the amount of shared information between two documents is a key to address a number of Natural Language Processing (NLP) challenges such as...

2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents

Publié en ligne: 30 mars 2018

Pages: 124 - 138

Reçu: 02 juil. 2017

Accepté: 25 nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0011

Mots clés
Plagiarism detection, intelligent plagiarism, fingerprinting, word embedding, Arabic language

© 2018 El Moatez Billah Nagoudi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents

Publié en ligne: 30 mars 2018

Pages: 124 - 138

Reçu: 02 juil. 2017

Accepté: 25 nov. 2017

DOI: https://doi.org/10.2478/cait-2018-0011

Mots clésPlagiarism detection, intelligent plagiarism, fingerprinting, word embedding, Arabic language

© 2018 El Moatez Billah Nagoudi et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Plagiarism detection, intelligent plagiarism, fingerprinting, word embedding, Arabic language