Structure-sensitive learning of text types

Authors:
Peter Geibel;Ulf Krumnack;Olga Pustylnikov;Alexander Mehler;Helmar Gust;Kai-Uwe Kühnberger
Affiliations:
University of Osnabrück, Institute of Cognitive Science, AI Group, Germany;University of Osnabrück, Institute of Cognitive Science, AI Group, Germany;University of Bielefeld, Text Technology Group, Germany;University of Bielefeld, Text Technology Group, Germany;University of Osnabrück, Institute of Cognitive Science, AI Group, Germany;University of Osnabrück, Institute of Cognitive Science, AI Group, Germany
Venue:
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Year:
2007

Citing 3
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Abstract

In this paper, we discuss the structure based classification of documents based on their logical document structure, i.e., their DOM trees.We describe a method using predefined structural features and also four tree kernels suitable for such structures. We evaluate the methods experimentally on a corpus containing the DOM trees of newspaper articles, and on the well-known SUSANNE corpus. We will demonstrate that, for the two corpora, many text types can be learned based on structural features only.