Computational Modeling as a Methodology for Studying Human Language Learning

  • Thierry Poibeau
  • Aline Villavicencio
  • Anna Korhonen
  • Afra Alishahi
Part of the Theory and Applications of Natural Language Processing book series (NLP)


The nature and amount of information needed for learning a natural language, and the underlying mechanisms involved in this process, are the subject of much debate: how is the knowledge of language represented in the human brain? Is it possible to learn a language from usage data only, or is some sort of innate knowledge and/or bias needed to boost the process? Are different aspects of language learned in order? These are topics of interest to (psycho)linguists who study human language acquisition, as well as to computational linguists who develop the knowledge sources necessary for large-scale natural language processing systems. Children are the ultimate subjects of any study of language learnability. They learn language with ease, in a short period of time and their acquired knowledge of language is flexible and robust.


Language Acquisition Specific Language Impairment Minimum Description Length Argument Structure Semantic Role 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Thierry Poibeau
    • 1
  • Aline Villavicencio
    • 2
  • Anna Korhonen
    • 3
    • 4
  • Afra Alishahi
    • 5
  1. 1.Laboratoire Langues, Textes, Traitements informatiques, Cognition, CNRSEcole Normale Supérieure and Université Sorbonne NouvelleParisFrance
  2. 2.Institute of InformaticsFederal University of Rio Grande do SulPorto AlegreBrazil
  3. 3.Computer LaboratoryUniversity of CambridgeCambridgeUK
  4. 4.Department of Theoretical and Applied Linguistics (DTAL)CambridgeUK
  5. 5.Department of Communication and Information StudiesTilburg UniversityTilburgThe Netherlands

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