Plagiarism is the act of taking another person's writing or idea without referring to the source of information.This is one of major problems in educational institutes. There is a number of plagiarism detection softwareavailable on the Internet. However, a few numbers of them works. Typically, they use a simple method forplagiarism detection e.g. string matching. The main weakness of this method is it cannot detect theplagiarism when the author replaces some words using synonyms. As such, this paper presents a newtechnique for a semantic-based plagiarism detection using Semantic Role Labeling (SRL) and termweighting. SRL is deployed in order to calculate the semantic-based similarity. The main different from theexisting framework is terms in a sentence are weighted dynamically depending on their roles in the sentence e.g. subject, verb or object. This technique enhances the plagiarism detection mechanism more efficientlythan existing system although positions of terms in a sentence are reordered. The experimental results showthat the proposed method can detect the plagiarism document more effective than the existing methods,Anti-kobpae, Turnit-in and Traditional Semantic Role Labeling.
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