Abstract
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%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.