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Probabilistic Anomaly Detection Method for Authorship Verification

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Book cover Statistical Language and Speech Processing (SLSP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8791))

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Abstract

Authorship verification is the task of determining if a given text is written by a candidate author or not. In this paper, we present a first study on using an anomaly detection method for the authorship verification task. We have considered a weakly supervised probabilistic model based on a multivariate Gaussian distribution. To evaluate the effectiveness of the proposed method, we conducted experiments on a classic French corpus. Our preliminary results show that the probabilistic method can achieve a high verification performance that can reach an F1 score of 85 %. Thus, this method can be very valuable for authorship verification.

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Notes

  1. 1.

    http://www.gutenberg.org/

  2. 2.

    http://gallica.bnf.fr/

  3. 3.

    http://ero.corneille-moliere.com/?p=page52&m=ero&l=fra

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Author information

Authors and Affiliations

  1. LIP6 (Laboratoire D’Informatique de Paris 6), Université Pierre et Marie Curie and CNRS (UMR7606), ACASA Team, 4 Place Jussieu, 75252, Paris Cedex 05, France

    Mohamed Amine Boukhaled & Jean-Gabriel Ganascia

Authors
  1. Mohamed Amine Boukhaled
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  2. Jean-Gabriel Ganascia
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Corresponding author

Correspondence to Mohamed Amine Boukhaled .

Editor information

Editors and Affiliations

  1. University Joseph Fourier, Grenoble, France

    Laurent Besacier

  2. Rovira i Virgili University, Tarragona, Spain

    Adrian-Horia Dediu

  3. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

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Boukhaled, M.A., Ganascia, JG. (2014). Probabilistic Anomaly Detection Method for Authorship Verification. In: Besacier, L., Dediu, AH., Martín-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2014. Lecture Notes in Computer Science(), vol 8791. Springer, Cham. https://doi.org/10.1007/978-3-319-11397-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-11397-5_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11396-8

  • Online ISBN: 978-3-319-11397-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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