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Self-Organizing Maps in Symbol Processing

  • Timo Honkela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1778)

Abstract

A symbol as such is disassociated from the world. In addition, as a discrete entity a symbol does not mirror all the details of the portion of the world that it is meant to refer to. Humans establish the association between the symbols and the referenced domain – the words and the world – through a long learning process in a community. This paper studies how Kohonen self-organizing maps can be used for modeling the learning process needed in order to create a conceptual space based on a relevant context with which the symbols are associated. The categories that emerge in the self-organizing process and their implicitness are considered as well as the possibilities to model contextuality, subjectivity and intersubjectivity of interpretation.

Keywords

Natural Language Processing Predicate Logic Model Vector Conceptual Space Word Sense Disambiguation 
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 2000

Authors and Affiliations

  • Timo Honkela
    • 1
  1. 1.Media LabUniversity of Art and DesignHelsinkiFinland

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