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Designing Semantic Kernels as Implicit Superconcept Expansions

  • Recently, there has been an increased interest in the exploitation of background knowledge in the context of text mining tasks, especially text classification. At the same time, kernel-based learning algorithms like Support Vector Machines have become a dominant paradigm in the text mining community. Amongst other reasons, this is also due to their capability to achieve more accurate learning results by replacing standard linear kernel (bag-of-words) with customized kernel functions which incorporate additional apriori knowledge. In this paper we propose a new approach to the design of ‘semantic smoothing kernels’ by means of an implicit superconcept expansion using well-known measures of term similarity. The experimental evaluation on two different datasets indicates that our approach consistently improves performance in situations where (i) training data is scarce or (ii) the bag-ofwords representation is too sparse to build stable models when using the linear kernel.

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Metadaten
Author:Stephan Bloehdorn, Roberto Basili, Marco Cammisa, Alessandro Moschitti
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-243
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/04/18
Contributing Corporation:Institute AIFB, University of Karlsruhe, Germany
Release Date:2011/04/18
Source:KDML 2006: Knowledge Discovery, Data Mining, and Machine Learning 9.-13. Oktober 2006, Hildesheim
PPN:Link zum Katalog
Contributor:Althoff, Klaus-Dieter
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoDeutsches Urheberrecht