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Natural Language Complexity and Machine Learning

  • Leonor Becerra-Bonache
  • M. Dolores Jiménez-LópezEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

Eventhough complexity is a central notion in linguistics, until recently, it has not been widely researched in the area. During the 20th century, linguistic complexity was supposed to be invariant. In general, recent work on language complexity takes an absolute perspective of the concept while the relative complexity approach –although considered as conceptually coherent– has hardly begun to be developed. In this paper, we introduce machine learning tools that can be used to calculate natural language complexity from a relative point of view by considering the process of first language acquisition.

Keywords

Complexity Natural language Machine learning 

Notes

Acknowledgments

This research has been supported by the Ministerio de Economía y Competitividad and the Fondo Europeo de Desarrollo Regional under the project number FFI2015-69978-P (MINECO/FEDER, UE) of the Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento.

The work of Leonor Becerra-Bonache has been performed during her teaching leave granted by the CNRS (French National Center for Scientific Research) in the Computer Science Department of Aix-Marseille University.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Leonor Becerra-Bonache
    • 1
  • M. Dolores Jiménez-López
    • 2
    Email author
  1. 1.CNRS, Laboratoire Hubert-Curien UMR 5516Univ. Lyon, UJM-St-EtienneSaint-ÉtienneFrance
  2. 2.Departament de Filologies Romániques, Research Group on Mathematical LinguisticsUniversitat Rovira i VirgiliTarragonaSpain

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