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Automatic Specialized vs. Non-specialized Text Differentiation: The Usability of Grammatical Features in a Latin Multilingual Context

  • M. Teresa Cabré
  • Iria da CunhaEmail author
  • Eric SanJuan
  • Juan-Manuel Torres-Moreno
  • Jorge Vivaldi
Chapter
  • 1.6k Downloads
Part of the Educational Linguistics book series (EDUL, volume 19)

Abstract

In this chapter it is shown that certain grammatical features, besides lexicon, have a strong potential to differentiate specialized texts from non-specialized texts. A tool including these features has been developed and it has been trained using machine learning techniques based on association rules using two sub-corpora (specialized vs. non-specialized), each one divided into training and test corpora. This tool has been evaluated and the results show that the used strategy is suitable to differentiate specialized texts from non-specialized texts. These results could be considered as an innovative perspective to research on domains related with terminology, specialized discourse and computational linguistics, with applications to automatic compilation of Languages for Specific Purposes (LSP) corpora and Adaptive Focused Information Retrieval (AFIR) among others.

Keywords

Grammatical Features Specialized Texts Association Rules Information Retrieval Focus Grammatical Tagging 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been partially financed by the Spanish projects RICOTERM 4 (FFI2010-21365-C03-01) and APLE 2 (FFI2012-37260), and a Juan de la Cierva grant (JCI-2011-09665).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • M. Teresa Cabré
    • 1
  • Iria da Cunha
    • 1
    Email author
  • Eric SanJuan
    • 2
    • 3
  • Juan-Manuel Torres-Moreno
    • 2
    • 3
    • 4
  • Jorge Vivaldi
    • 1
  1. 1.Institut Universitari de Lingüística AplicadaUniversidad Pompeu Fabra (UPF)BarcelonaSpain
  2. 2.Departament Statistique et Informatique Décisionnelle, Laboratoire Informatique d’AvignonUniversité d’Avignon (UAPV)AvignonFrance
  3. 3.Brain and Language Research Institute (BLRI)AvignonFrance
  4. 4.École Polytechnique de MontréalMontrealCanada

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