Ghiglieri, Marco ; Fürnkranz, Johannes (2009)
Learning To Recognize Missing E-mail Attachments.
Report, Bibliographie
Kurzbeschreibung (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
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2009 |
| Autor(en): | Ghiglieri, Marco ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Learning To Recognize Missing E-mail Attachments |
| Sprache: | Deutsch |
| Publikationsjahr: | Mai 2009 |
| (Heft-)Nummer: | TUD-KE-2009-05 |
| Zugehörige Links: | |
| Kurzbeschreibung (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 |
| ID-Nummer: | TUD-CS-2009-1910 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > SECUSO - Security, Usability and Society 20 Fachbereich Informatik > Sicherheit in der Informationstechnik LOEWE LOEWE > LOEWE-Zentren LOEWE > LOEWE-Zentren > CASED – Center for Advanced Security Research Darmstadt |
| Hinterlegungsdatum: | 31 Dez 2016 11:42 |
| Letzte Änderung: | 17 Jul 2018 09:11 |
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