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AbstractHelpdesks have to manage a huge amount of support requests which are usually submitted via e-mail. In order to be assigned to experts e ciently, incoming e-mails have to be classi- ed w. r. t. several facets, in particular topic, support type and priority. It is desirable to perform these classi cations automatically. We report on experiments using Support Vector Machines and k-Nearest-Neighbours, respectively, for the given multi-facet classi - cation task. The challenge is to de ne suitable features for each facet. Our results suggest that improvements can be gained for all facets, and they also reveal which features are promising for a particular facet.
CitationBeckers, T., Frommholz, I. Bönning, R. (2009) 'Multi-facet Classification of E-Mails in a Helpdesk Scenario,' in Proc. of the GI Information Retrieval Workshop at LWA 2009
PublisherGesellschaft für Informatik e.V.
TypeConference papers, meetings and proceedings
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- Creative Commons
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by-nc-nd/4.0/