2.50
Hdl Handle:
http://hdl.handle.net/10547/275694
Title:
Multi-facet classification of e-mails in a helpdesk scenario
Authors:
Beckers, Thomas; Frommholz, Ingo ( 0000-0002-5622-5132 ) ; Bonning, Ralf
Abstract:
Helpdesks 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.
Affiliation:
University of Duisburg-Essen, Germany; University of Glasgow; d.velop AG
Citation:
Beckers, 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
Publisher:
Gesellschaft für Informatik e.V.
Issue Date:
2009
URI:
http://hdl.handle.net/10547/275694
Additional Links:
http://www.is.inf.uni-due.de/bib/docs/Beckers_etal_09.html.en
Type:
Conference papers, meetings and proceedings
Language:
en
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorBeckers, Thomasen_GB
dc.contributor.authorFrommholz, Ingoen_GB
dc.contributor.authorBonning, Ralfen_GB
dc.date.accessioned2013-03-22T12:35:49Z-
dc.date.available2013-03-22T12:35:49Z-
dc.date.issued2009-
dc.identifier.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 2009en_GB
dc.identifier.urihttp://hdl.handle.net/10547/275694-
dc.description.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.en_GB
dc.language.isoenen
dc.publisherGesellschaft für Informatik e.V.en_GB
dc.relation.urlhttp://www.is.inf.uni-due.de/bib/docs/Beckers_etal_09.html.enen_GB
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjecthelpdesksen_GB
dc.subjectclassificationen_GB
dc.subjectSupport Vector Machinesen_GB
dc.subjectk-Nearest-Neighboursen_GB
dc.subjectemailen_GB
dc.subjectfaceten_GB
dc.titleMulti-facet classification of e-mails in a helpdesk scenarioen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Duisburg-Essen, Germanyen_GB
dc.contributor.departmentUniversity of Glasgowen_GB
dc.contributor.departmentd.velop AGen_GB
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