A Mixed Fuzzy Similarity Approach to Detect Plagiarism in Persian Texts

A variety of methods and metrics have been offered so far to measure the extent of similarity among various documents and plagiarism detection systems. However, most of them do not take ambiguity inherent in natural language into account. Therefore, in this paper, a new method taking lexical and semantic features and similarity measures into consideration has been proposed. In the first step, after preprocessing and removing stop word, a text was divided into two parts: general and domain-specific knowledge words. Then, the mixed lexical and semantic fuzzy inference system was designed to assess text similarity. The proposed method was evaluated on Persian paper abstracts of International Conference on e-Learning and e-Teaching (ICELET) Corpus and using IT domain knowledge ontology. The results indicated that the proposed method can achieve a rate of 79% in terms of precision and can detect 83% of the plagiarism cases.