TY - JOUR TI - A Persian Fuzzy Plagiarism Detection Approach JO - Journal of Information Systems and Telecommunication (JIST) JA - Iranian Academic Center for Education,Culture and Research LA - en SN - 2322-1437 AU - shima rakian AU - Faramarz Safi Esfahani AU - Hamid Rastegari AD - دانشجوی کارشناسی ارشد AD - Department of Software Engineering, Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran AD - Najaf Abad Branch, Islamic Azad University, Najaf Abad, Iran Y1 - 2015 PY - 2015 VL _ 11 IS - 1 SP - 1 EP - 10 KW - text retrieval KW - plagiarism detection KW - external plagiarism detection KW - text similarity KW - fuzzy similarity detection KW - KW - KW - DO - 10.7508/jist.2015.03.007 N2 - Plagiarism is one of the common problems that is present in all organizations that deal with electronic content. At present, plagiarism detection tools, only detect word by word or exact copy phrases and paraphrasing is often mixed. One of the successful and applicable methods in paraphrasing detection is fuzzy method. In this study, a new fuzzy approach has been proposed to detect external plagiarism in Persian texts which is called Persian Fuzzy Plagiarism Detection (PFPD). The proposed approach compares paraphrased texts with the aim to recognize text similarities. External plagiarism detection, evaluates through a comparison between query document and a document collection. To avoid un-necessary comparisons this tool employs intelligent technology for comparing, suspicious documents, in different levels hierarchically. This method intends to conformed Fuzzy model to Persian language and improves previous methods to evaluate similarity degree between two sentences. Experiments on three corpora TMC, Irandoc and extracted corpus from prozhe.com, are performed to get confidence on proposed method performance. The obtained results showed that using proposed method in candidate documents retrieval, and in evaluating text similarity, increases the precision, recall and F measurement in comparing with one of the best previous fuzzy methods, respectively 22.41, 17.61, and 18.54 percent on the average. UR - http://rimag.ir/fa/Article/14878 L1 - http://rimag.ir/fa/Article/Download/14878 TY -JOURId - 14878