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A BioInspired Model for Parsing of Natural Languages

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Nature Inspired Cooperative Strategies for Optimization (NICSO 2007)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 129))

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Abstract

Networks of Evolutionary Processors (NEPs) –introduced in Castellanos et al. (2001)– are a new computing mechanism directly inspired in the behaviour of cell populations. In the paper, we explain why Networks of Evolutionary Processors can be suitable for modelling natural language –an entity generated in parallel by a modular architecture– and specially syntax –a modular device of specialized processors inside the modular construct of language. An implementation of NEPs for parsing of simple structures [[NP] V [NP]] is also suggested. By applying this theory to natural language, we introduce a new line of research in formal linguistics.

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Author information

Authors and Affiliations

  1. Research Group on Mathematical Linguistics, Rovira i Virgili University, Pl. Imperial Tarraco, 1, 43005, Tarragona, Spain

    Gemma Bel-Enguix & M. Dolores Jiménez-López

Authors
  1. Gemma Bel-Enguix
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  2. M. Dolores Jiménez-López
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Editor information

Editors and Affiliations

  1. School of Computer Sciences and Information Technology, University of Nottingham, Jubilee Campus, Nottingham, NG81BB, UK

    Natalio Krasnogor

  2. Department of Mathematics and Computer Science, University of Catania, v.le A. Doria, 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Mario Pavone  & 

  3. Department of Computer Science and Artificial Intelligence E.T.S. Ingenieria Informatica C/ Periodista Daniel Saucedo Aranda s/n, University of Granada, 18071, Granada, Spain

    David Pelta

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© 2008 Springer-Verlag Berlin Heidelberg

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Bel-Enguix, G., Jiménez-López, M.D. (2008). A BioInspired Model for Parsing of Natural Languages. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2007). Studies in Computational Intelligence, vol 129. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78987-1_33

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  • DOI: https://doi.org/10.1007/978-3-540-78987-1_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78986-4

  • Online ISBN: 978-3-540-78987-1

  • eBook Packages: EngineeringEngineering (R0)

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