Abstract
Cosine similarity measure is an efficient plagiarism detection algorithm for documents. However, it may be misled if the document is not properly preprocessed. Furthermore, the weight for the words in the document should depend on its occurrence frequency in the whole digital library. Otherwise, cosine similarity measure may not accurate enough. This paper aims to enhance the accuracy of similarity measure. A preprocessing method and a model to adjust word’s weight according to occurrence frequency are proposed in this paper. The paper also develops a sample to illustrate how to preprocess documents, adjust the weight for the words and calculate the similarity. The sample shows that it gets better result after applying the model in this paper.
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Fang, J., Zhang, Y. (2013). An Improved Plagiarism Detection Method: Model and Sample. In: Wong, W.E., Ma, T. (eds) Emerging Technologies for Information Systems, Computing, and Management. Lecture Notes in Electrical Engineering, vol 236. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7010-6_106
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DOI: https://doi.org/10.1007/978-1-4614-7010-6_106
Publisher Name: Springer, New York, NY
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