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The Processing & Recognition of Symbol Sequences Mark W. Andrews (mwa1@cornell.edu)
 

Summary: The Processing & Recognition of Symbol Sequences
Mark W. Andrews (mwa1@cornell.edu)
Department of Psychology; Uris Hall
Ithaca, NY 14853 USA
Abstract
It is proposed that learning a language (or more gener-
ally, a sequence of symbols) is formally equivalent to
reconstructing the state-space of a non-linear dynamical
system. Given this, a set of results from the study of
nonlinear dynamical systems may be especially relevant
for an understanding of the mechanisms underlying lan-
guage processing. These results demonstrate that a dy-
namical system can be reconstructed on the basis of the
data that it emits. They imply that with minimal assump-
tions the structure of an arbitrary language can be inferred
entirely from a corpus of data. State-Space reconstruc-
tion can implemented in a straightforward manner in a
model neural system. Simulations of a recurrent neural
network, trained on a large corpus of natural language,
are described. Results imply that the network sucessfully

  

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

 

Collections: Computer Technologies and Information Sciences