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Summary: Inferring a Probabilistic Model of Semantic Memory from Word
Association Norms
Mark Andrews (m.andrews@ucl.ac.uk)
David Vinson (d.vinson@ucl.ac.uk)
Gabriella Vigliocco (g.vigliocco@ucl.ac.uk)
Cognition, Perceptual and Brain Sciences
University College London,
26 Bedford Way
London, WC1H 0AP
United Kingdom
Abstract
In this paper, we introduce a method of data-analysis for
word association norms. The defining characteristic of
this method is that is based upon the inference of a prob-
abilistic generative model of word-associations. The in-
ferred model can in principle provide a clear and in-
tuitive representation of the semantic knowledge inher-
ent in word association data, facilitate an understand-
ing of the process by which word associations are gen-
erated, extrapolate beyond the observed data to make
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