Abstract
Currently, a large number of documents are created as digital material and distributed world-wide. Digital materials are easy to publish and copy at a remarkably low cost. As a result, many documents are copied illegally, and this practice is spreading, making plagiarism a significant social issue. Therefore, the need to develop systems that detect plagiarism is very high. We have developed a new plagiarism detection method that compares documents by using approximate string matching to detect plagiarism. We have also developed a technique that reduces the computational time of the comparison method. In this paper, we demonstrate our proposed method’s usefulness through experiments and through the measuring indexes of precision and recall.
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This work was supported by JSPS KAKENHI Grant Number 15K00426.
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Nakatoh, T., Minami, T. (2018). Reducing Computational Effort for Plagiarism Detection with Approximate String Matching. In: Ghazali, R., Deris, M., Nawi, N., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2018. Advances in Intelligent Systems and Computing, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-72550-5_41
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DOI: https://doi.org/10.1007/978-3-319-72550-5_41
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