Abstract
In this paper, we propose a novel approach that uses a weighted directed multigraph for text pattern recognition. Instead of the traditional model which is based on the frequency of keywords for text classification, we set up a weighted directed multigraph model using the distances between the keywords as the weights of arcs. We then developed a keyword-frequencydistance- based algorithm which not only utilizes the frequency information of keywords but also their ordering information. We applied this new idea to the detection of plagiarized papers and the detection of fraudulent emails written by the same person. The results on these case studies show that this new method performs much better than traditional methods.
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Wu, Q., Fuller, E., Zhang, CQ. (2010). Graph Model for Pattern Recognition in Text. In: Ting, IH., Wu, HJ., Ho, TH. (eds) Mining and Analyzing Social Networks. Studies in Computational Intelligence, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13422-7_1
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DOI: https://doi.org/10.1007/978-3-642-13422-7_1
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