NEW APPROACH FOR PLAGIARISM DETECTION
Dan Lemberg, Avi Soffer, Zeev Volkovich · International Journal of Apllied Mathematics · 2016
The paper proposes a new approach for intrinsic plagiarism detection, based on a new unique method, which enables identifying style changes in a text using novel chronology-based similarity measures.A model for finding significant deviations in the style across a given document is constructed aiming to indicate text parts which are suspected to be written by co-authors, or to be devoted to a different thematic, or to be a plagiarism.We consider each segment as "result of the text evolution" provided by its predecessors in the text.Resting upon this evolution standpoint, the metric evaluating dissimilarity between two given segments is introduced, and a text is clustered using this measure aiming to turn out disparity of the text.We also propose a new clustering procedure involving an embedding of data in an Euclidean space with subsequent clustering using the K-means approach.The obtained results demonstrate high ability of the method.