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Boosting for Text Classification with Semantic Features

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Book cover Advances in Web Mining and Web Usage Analysis (WebKDD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3932))

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Abstract

Current text classification systems typically use term stems for representing document content. Semantic Web technologies allow the usage of features on a higher semantic level than single words for text classification purposes. In this paper we propose such an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting, a successful machine learning technique is used for classification. Comparative experimental evaluations in three different settings support our approach through consistent improvement of the results. An analysis of the results shows that this improvement is due to two separate effects.

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Author information

Authors and Affiliations

  1. Institute AIFB, Knowledge Management Research Group, University of Karlsruhe, Germany

    Stephan Bloehdorn

  2. Knowledge and Data Engineering Group, University of Kassel, Germany

    Andreas Hotho

Authors
  1. Stephan Bloehdorn
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  2. Andreas Hotho
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Editor information

Editors and Affiliations

  1. Center for Web Intelligence School of Computing, DePaul University, Chicago, Illinois, USA

    Bamshad Mobasher

  2. Speed School of Engineering, Department of Computer Engineering & Computer Science, University of Louisville, KY 40292, Louisville, USA

    Olfa Nasraoui

  3. College of Architecture and Urban Planning, Tongji University, 1239 Siping Road, 200092, Shanghai, P.R. China

    Bing Liu

  4. Data Miners Inc., 77 North Washington Street, MA 02114, Boston, USA

    Brij Masand

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© 2006 Springer-Verlag Berlin Heidelberg

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Bloehdorn, S., Hotho, A. (2006). Boosting for Text Classification with Semantic Features. In: Mobasher, B., Nasraoui, O., Liu, B., Masand, B. (eds) Advances in Web Mining and Web Usage Analysis. WebKDD 2004. Lecture Notes in Computer Science(), vol 3932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11899402_10

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  • DOI: https://doi.org/10.1007/11899402_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47127-1

  • Online ISBN: 978-3-540-47128-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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