Abstract
This paper introduces a plagiarism detection scheme based on a Fuzzy Inference System and Semantic Role Labeling (FIS-SRL). The proposed technique 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. Voting for each argument generated by the FIS in order to select important arguments is also another feature of the proposed method. It has been concluded that not all arguments in the text affect the plagiarism detection process. Therefore, only the most important arguments were selected by the FIS, and the results have been used in the similarity calculation process. Experimental tests have been applied on the PAN-PC-09 data set and the results shows that the proposed method exhibits a better performance than the available recent methods of plagiarism detection, in terms of Recall, Precision and F-measure.
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Osman, A.H., Salim, N., Kumar, Y.J., Abuobieda, A. (2012). Fuzzy Semantic Plagiarism Detection. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_54
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DOI: https://doi.org/10.1007/978-3-642-35326-0_54
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