Abstract
Email classification is challenging due to its sparse and noisy feature space. To address this problem, a novel feature space enriching (FSE) technique based on two semantic knowledge bases is proposed in this paper. The basic idea of FSE is to select the related semantic features that will increase the global information for learning algorithms from the semantic knowledge bases, and use them to enrich the original sparse feature space. The resulting feature space of FSE can provide semantic-richer features for classification algorithms to learn improved classifiers. Naive Bayes and support vector machine are selected as the classification algorithms. Experiments on a bilingual enterprise email dataset have shown that: (1) the FSE technique can improve the email classification accuracy, especially for the sparse classes, (2) the SVM classifier benefits more from FSE than the naive Bayes classifier, (3) with the support of domain knowledge, the FSE technique can be more effective.
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Ye, Y., Ma, F., Rong, H., Huang, J. (2004). Enhanced Email Classification Based on Feature Space Enriching. In: Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2004. Lecture Notes in Computer Science, vol 3136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27779-8_26
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DOI: https://doi.org/10.1007/978-3-540-27779-8_26
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