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Learning Methods for Graph Models of Document Structure

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Modeling, Learning, and Processing of Text Technological Data Structures

Part of the book series: Studies in Computational Intelligence ((SCI,volume 370))

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Abstract

This chapter focuses on the structure-based classification of websites according to their hypertext type or genre. A website usually consists of several web pages. Its structure is given by their hyperlinks resulting in a directed graph. In order to represent the logical structure of a website, the underlying graph structure is represented as a so-called directed Generalized Tree (GT), in which a rooted spanning tree represents the logical core structure of the site. The remaining arcs are classified as reflexive, lateral, and vertical up- and downward arcs with respect to this kernel tree.

We consider unsupervised and supervised approaches for learning classifiers from a given web corpus. Quantitative Structure Analysis (QSA) is based on describing GTs using a series of attributes that characterize their structural complexity, and employs feature selection combined with unsupervised learning techniques. Kernel methods – the second class of approaches we consider – focus on typical substructures characterizing the classes. We present a series of tree, graph and GT kernels that are suitable for solving the problem and discuss the problem of scalability. All learning approaches are evaluated using a web corpus containing classified websites.

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Authors and Affiliations

  1. Fak. IV, TU Berlin, Straße des 17. Juni 135, D-10623, Berlin, Germany

    Peter Geibel

  2. Computer Science and Informatics, Goethe-Universität Frankfurt, Senckenberganlage 31, D-60325, Frankfurt am Main, Germany

    Alexander Mehler

  3. Institute of Cognitive Science, Universität Osnabrück, Albrechtstraße 28, D-49076, Osnabrück, Germany

    Kai-Uwe Kühnberger

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  1. Peter Geibel
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  2. Alexander Mehler
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Editor information

Editors and Affiliations

  1. Faculty of Linguistics and Literature, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany

    Alexander Mehler

  2. Institute of Cognitive Science, University of Osnabrück, Albrechtstr. 28, 49076, Osnabrück, Germany

    Kai-Uwe Kühnberger

  3. Angewandte Sprachwissenschaft und, Justus-Liebig-Universität Gießen, Computerlinguistik, Otto-Behaghel-Straße 10D, 35394, Gießen, Germany

    Henning Lobin  & Harald Lüngen  & 

  4. Institut für deutsche Sprache und Literatur, Technical University Dortmund, Emil-Figge-Straße 50, 44227, Dortmund, Germany

    Angelika Storrer

  5. SFB 441 Linguistic Data Structures, Eberhard Karls Universität Tübingen, Nauklerstraße 35, 72074, Tübingen, Germany

    Andreas Witt

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Geibel, P., Mehler, A., Kühnberger, KU. (2011). Learning Methods for Graph Models of Document Structure. In: Mehler, A., Kühnberger, KU., Lobin, H., Lüngen, H., Storrer, A., Witt, A. (eds) Modeling, Learning, and Processing of Text Technological Data Structures. Studies in Computational Intelligence, vol 370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22613-7_14

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  • DOI: https://doi.org/10.1007/978-3-642-22613-7_14

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