Classification of Documents Based on the Structure of Their DOM Trees

Authors:
Peter Geibel;Olga Pustylnikov;Alexander Mehler;Helmar Gust;Kai-Uwe Kühnberger
Affiliations:
Institute of Cognitive Science, AI Group, University of Osnabrück, Germany;Text Technology Group, University of Bielefeld, Germany;Text Technology Group, University of Bielefeld, Germany;Institute of Cognitive Science, AI Group, University of Osnabrück, Germany;Institute of Cognitive Science, AI Group, University of Osnabrück, Germany
Venue:
Neural Information Processing
Year:
2008

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Abstract

In this paper, we discuss kernels that can be applied for the classification of XML documents based on their DOM trees. DOM trees are ordered trees in which every node might be labeled by a vector of attributes including its XML tag and the textual content. We describe five new kernels suitable for such structures: a kernel based on predefined structural features, a tree kernel derived from the well-known parse tree kernel, the set tree kernel that allows permutations of children, the string tree kernel being an extension of the so-called partial tree kernel, and the soft tree kernel as a more efficient alternative. We evaluate the kernels experimentally on a corpus containing the DOM trees of newspaper articles and on the well-known SUSANNE corpus.