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Integrating Attributional and Distributional Information in a Probabilistic Model of Meaning Representation
 

Summary: Integrating Attributional and Distributional Information
in a Probabilistic Model of Meaning Representation
Mark Andrews (mark@gatsby.ucl.ac.uk)
Gatsby Computational Neuroscience Unit, University College London
London, WC1N 3AR
Gabriella Vigliocco (g.vigliocco@ucl.ac.uk)
David Vinson (d.vinson@ucl.ac.uk)
Department of Psychology, University College London
London, WC1E 6BT
Abstract
In this paper we present models of how meaning is represented in the brain/mind, based
upon the assumption that children develop meaning representations for words using two main
sources of information: information derived from their concrete experience with objects and
events in the world (which we refer to as attributional information) and information implic-
itly derived from exposure to language (which we refer to as distributional information). In
the first part of the paper we present a model developed using self-organising maps (SOMs)
starting from speaker-generated features (properties that speakers considered to be important
in defining and describing the meaning of a word). This model captures meaning similarity
between words based solely upon attributional information and has been shown to be success-
ful in predicting a number of behavioural semantic effects. In the second part of the paper,

  

Source: Andrews, Mark W. - Department of Cognitive, Perceptual and Brain Sciences, University College London
Vigliocco, Gabriella - Department of Psychology, University College London

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences