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A Corpus Balancing Method for Language Model Construction

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Computational Linguistics and Intelligent Text Processing (CICLing 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2588))

Abstract

The language model is an important component of any speech recognition system. In this paper, we present a lexical enrichment methodology of corpora focused on the construction of statistical language models. This methodology considers, on one hand, the identification of the set of poor represented words of a given training corpus, and on the other hand, the enrichment of the given corpus by the repetitive inclusion of selected text fragments containing these words. The first part of the paper describes the formal details about this methodology; the second part presents some experiments and results that validate our method.

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Author information

Authors and Affiliations

  1. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Mexico

    Luis Villaseñor-Pineda, Manuel Montes-y-Gómez & Manuel Alberto Pérez-Coutiño

  2. Laboratoire CLIPS-IMAG, Université Joseph Fourier, France

    Dominique Vaufreydaz

Authors
  1. Luis Villaseñor-Pineda
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  2. Manuel Montes-y-Gómez
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  3. Manuel Alberto Pérez-Coutiño
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  4. Dominique Vaufreydaz
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Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación (CIC), Instituto Politécnico Nacional (IPN), Col. Zacatenco, CP 07738, Mexico D.F., Mexico

    Alexander Gelbukh

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© 2003 Springer-Verlag Berlin Heidelberg

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Villaseñor-Pineda, L., Montes-y-Gómez, M., Pérez-Coutiño, M.A., Vaufreydaz, D. (2003). A Corpus Balancing Method for Language Model Construction. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2003. Lecture Notes in Computer Science, vol 2588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36456-0_40

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  • DOI: https://doi.org/10.1007/3-540-36456-0_40

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00532-2

  • Online ISBN: 978-3-540-36456-6

  • eBook Packages: Springer Book Archive

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