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Tensor Space Models for Authorship Identification

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Artificial Intelligence: Theories, Models and Applications (SETN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5138))

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Abstract

Authorship identification can be viewed as a text categorization task. However, in this task the most frequent features appear to be the most important discriminators, there is usually a shortage of training texts, and the training texts are rarely evenly distributed over the authors. To cope with these problems, we propose tensors of second order for representing the stylistic properties of texts. Our approach requires the calculation of much fewer parameters in comparison to the traditional vector space representation. We examine various methods for building appropriate tensors taking into account that similar features should be placed in the same neighborhood. Based on an existing generalization of SVM able to handle tensors we perform experiments on corpora controlled for genre and topic and show that the proposed approach can effectively handle cases where only limited training texts are available.

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Authors and Affiliations

  1. Dept. of Information and Communication Systems Eng., University of the Aegean, 83200, Karlovassi, Greece

    Spyridon Plakias & Efstathios Stamatatos

Authors
  1. Spyridon Plakias
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  2. Efstathios Stamatatos
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Editor information

John Darzentas George A. Vouros Spyros Vosinakis Argyris Arnellos

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Plakias, S., Stamatatos, E. (2008). Tensor Space Models for Authorship Identification. In: Darzentas, J., Vouros, G.A., Vosinakis, S., Arnellos, A. (eds) Artificial Intelligence: Theories, Models and Applications. SETN 2008. Lecture Notes in Computer Science(), vol 5138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87881-0_22

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  • DOI: https://doi.org/10.1007/978-3-540-87881-0_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87880-3

  • Online ISBN: 978-3-540-87881-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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