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Learning To Recognize Missing E-mail Attachments

Ghiglieri, Marco and Fürnkranz, Johannes (2009):
Learning To Recognize Missing E-mail Attachments.
(TUD-KE-2009-05), [Report]

Abstract

Forgotten attachments of e-mail message are a common and obnoxious problem. Several E-mail readers provide plugins that attempt to tackle this problem by trying to guess whether a message needs an attachment and warn the user in case s/he does not attach a file to such a message. However, these approaches essentially only work with a fixed list of keywords, which trigger such a warning whenever they occur in a message. In this paper, we try conventional machine learning techniques, which have been previously shown to work well for related problems such as spam mail filtering, on this new problem. Our results show that they work very well, clearly outperforming simple keyword-based approaches. The software is available as plugin for the Thunderbird e-mail reader

Item Type: Report
Erschienen: 2009
Creators: Ghiglieri, Marco and Fürnkranz, Johannes
Title: Learning To Recognize Missing E-mail Attachments
Language: German
Abstract:

Forgotten attachments of e-mail message are a common and obnoxious problem. Several E-mail readers provide plugins that attempt to tackle this problem by trying to guess whether a message needs an attachment and warn the user in case s/he does not attach a file to such a message. However, these approaches essentially only work with a fixed list of keywords, which trigger such a warning whenever they occur in a message. In this paper, we try conventional machine learning techniques, which have been previously shown to work well for related problems such as spam mail filtering, on this new problem. Our results show that they work very well, clearly outperforming simple keyword-based approaches. The software is available as plugin for the Thunderbird e-mail reader

Number: TUD-KE-2009-05
Divisions: 20 Department of Computer Science
20 Department of Computer Science > SECUSO - Security, Usability and Society
20 Department of Computer Science > Security, Usability and Society
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt
Date Deposited: 31 Dec 2016 11:42
Identification Number: TUD-CS-2009-1910
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