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Measuring Global Similarity Between Texts

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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

We propose a new similarity measure between texts which, contrary to the current state-of-the-art approaches, takes a global view of the texts to be compared. We have implemented a tool to compute our textual distance and conducted experiments on several corpuses of texts. The experiments show that our methods can reliably identify different global types of texts.

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Notes

  1. 1.

    http://pdos.csail.mit.edu/scigen/

  2. 2.

    http://www.kongshoj.net/automogensen/

  3. 3.

    http://wordnet.princeton.edu/

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Authors and Affiliations

  1. Inria/IRISA Rennes, Rennes, France

    Uli Fahrenberg, Fabrizio Biondi, Kevin Corre, Cyrille Jegourel & Axel Legay

  2. University College of Northern Denmark, Aalborg, Denmark

    Simon Kongshøj

Authors
  1. Uli Fahrenberg
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  2. Fabrizio Biondi
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  3. Kevin Corre
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  4. Cyrille Jegourel
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  5. Simon Kongshøj
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  6. Axel Legay
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Corresponding author

Correspondence to Uli Fahrenberg .

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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© 2014 Springer International Publishing Switzerland

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Fahrenberg, U., Biondi, F., Corre, K., Jegourel, C., Kongshøj, S., Legay, A. (2014). Measuring Global Similarity Between Texts. 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_17

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  • DOI: https://doi.org/10.1007/978-3-319-11397-5_17

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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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