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A Grammatical Inference Model for Measuring Language Complexity

  • Leonor Becerra-Bonache
  • M. Dolores Jiménez-LópezEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

The 21st century has re-opened the interest of Linguistics on the complexity of natural languages. The equi-complexity dogma –the idea that all languages must be equally complex– has been challenged by a number of researchers that claim that indeed natural languages differ in complexity. In the last fifteen years, challengers of the equi-complexity dogma have proposed many complexity measures that depend on their way of defining complexity. In this paper, we propose a grammatical inference model to measure the relative complexity of languages. The computational tool we introduce is the result of an interdisciplinary study inspired in the process of natural language acquisition.

Keywords

Linguistic complexity Relative complexity Grammatical inference Machine Learning 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Leonor Becerra-Bonache
    • 1
  • M. Dolores Jiménez-López
    • 2
    Email author
  1. 1.Laboratoire Hubert CurienJean Monnet UniversitySaint-EtienneFrance
  2. 2.Research Group on Mathematical LinguisticsUniversitat Rovira i VirgiliTarragonaSpain

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