2L-APD: A Two-Level Plagiarism Detection System for Arabic Documents
| Autor: | El Moatez Billah Nagoudi, Ahmed Khorsi, Hadda Cherroun, Didier Schwab |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
General Computer Science
Arabic business.industry Computer science Information technology 02 engineering and technology computer.software_genre language.human_language 020204 information systems 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Plagiarism detection Artificial intelligence business computer Natural language processing |
| Zdroj: | Cybernetics and Information Technologies. 18:124-138 |
| ISSN: | 1314-4081 |
| DOI: | 10.2478/cait-2018-0011 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
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