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C. D. Spyropoulos

Software & Knowledge Engineering Lab, Institute of Informatics & Telecommunications, NCSR Demokritos, Greece

Stacking classifiers for anti-spam filtering of e-mail

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Jun 19, 2001
G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C. D. Spyropoulos, P. Stamatopoulos
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We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.

* Proceedings of "Empirical Methods in Natural Language Processing" (EMNLP 2001), L. Lee and D. Harman (Eds.), pp. 44-50, Carnegie Mellon University, Pittsburgh, PA, 2001  
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Resolving Part-of-Speech Ambiguity in the Greek Language Using Learning Techniques

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Jun 30, 1999
G. Petasis, G. Paliouras, V. Karkaletsis, C. D. Spyropoulos, I. Androutsopoulos
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This article investigates the use of Transformation-Based Error-Driven learning for resolving part-of-speech ambiguity in the Greek language. The aim is not only to study the performance, but also to examine its dependence on different thematic domains. Results are presented here for two different test cases: a corpus on "management succession events" and a general-theme corpus. The two experiments show that the performance of this method does not depend on the thematic domain of the corpus, and its accuracy for the Greek language is around 95%.

* In Fakotakis, N. et al. (Eds.), Machine Learning in Human Language Technology (Proceedings of the ACAI Workshop), pp. 29-34, Chania, Greece, 1999.  
* 6 pages. To appear in the Proceedings of the ECCAI Advanced Course on Artificial Intelligence(ACAI'99), Chania, Greece, July 1999 
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