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Integrating Experiential and Distributional Data to Learn Semantic Representations
 

Summary: Integrating Experiential and Distributional Data to Learn
Semantic Representations
Mark Andrews, Gabriella Vigliocco, and David Vinson
University College London
The authors identify 2 major types of statistical data from which semantic representations can be learned.
These are denoted as experiential data and distributional data. Experiential data are derived by way of
experience with the physical world and comprise the sensory-motor data obtained through sense
receptors. Distributional data, by contrast, describe the statistical distribution of words across spoken and
written language. The authors claim that experiential and distributional data represent distinct data types
and that each is a nontrivial source of semantic information. Their theoretical proposal is that human
semantic representations are derived from an optimal statistical combination of these 2 data types. Using
a Bayesian probabilistic model, they demonstrate how word meanings can be learned by treating
experiential and distributional data as a single joint distribution and learning the statistical structure that
underlies it. The semantic representations that are learned in this manner are measurably more realis-
tic--as verified by comparison to a set of human-based measures of semantic representation--than those
available from either data type individually or from both sources independently. This is not a result of
merely using quantitatively more data, but rather it is because experiential and distributional data are
qualitatively distinct, yet intercorrelated, types of data. The semantic representations that are learned are
based on statistical structures that exist both within and between the experiential and distributional data
types.

  

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