Abstract
Most of the text mining systems are based on statistical analysis of term frequency. The statistical analysis of term (phrase or word) frequency captures the importance of the term within a document, but the techniques that had been proposed by now still need to be improved in terms of their ability to detect the plagiarized parts, especially for capturing the importance of the term within a sentence. Two terms can have a same frequency in their documents, but one term pays more to the meaning of its sentences than the other term. In this paper, we want to discriminate between the important term and unimportant term in the meaning of the sentences in order to adopt for idea plagiarism detection. This paper introduces an idea plagiarism detection based on semantic meaning frequency of important terms in the sentences. The suggested method analyses and compares text based on a semantic allocation for each term inside the sentence. SRL offers significant advantages when generating arguments for each sentence semantically. Promising experimental has been applied on the CS11 dataset and results revealed that the proposed technique's performance surpasses its recent peer methods of plagiarism detection in terms of Recall, Precision and F-measure.