Abstract
The automatic text categorization (ATC) is an emerging field that is technically based on the machine learning and information retrieval. Traditional machine learning usually builds a model that captures the behavior of the ‘concept’ from the training instances. Following this context, the ATC learns the concept from text data such as digital text documents, web pages, or e-mail texts. In this paper, we categorize the concepts that have been dealt by ATC into two groups: broad concepts and narrow concepts. Broad concepts are difficult to learn than narrow concepts. We propose a multi model approach where multiple local models are constructed to learn the broad concepts. Some experimental results showed that the multi model approach turned out to be effective in learning the confidentiality of company.
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Hwang, B., Lee, B. (2004). An Efficient E-mail Monitoring System for Detecting Proprietary Information Outflow Using Broad Concept Learning. In: Hicks, D.L. (eds) Metainformatics. MIS 2003. Lecture Notes in Computer Science, vol 3002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24647-3_6
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DOI: https://doi.org/10.1007/978-3-540-24647-3_6
Publisher Name: Springer, Berlin, Heidelberg
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