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Proceedings Paper

Artificial neural network versus case-based approaches to lexical combination
Author(s): George L. Dunbar
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Paper Abstract

Lexical combination presents a number of intriguing problems for cognitive science. By studying the empirical phenomena of combination we can derive constraints on models of the representation of individual lexical items. One particular phenomenon that symbolic models have been unable to accommodate is `semantic interaction'. Medin & Shoben (1988) have shown that properties associated with nouns by subjects vary with the choice of adjective. For example, wooden spoons are not just made of a different material: the phrase is interpreted as denoting a `larger' object. However, the adjective wooden is not generally held to carry implications as to size. We report experimental results showing similar effects across a range of properties for a single adjective in combination with different nouns from a single semantic field. It is this more radical dependence of interpretative features on lexical partners that we term `semantic interaction'. The phenomenon described by Medin and Shoben cannot be accounted for by the Selective Modification model, the most complete model hitherto. We show that a case-based reasoning system could account for earlier data because of the particular examples chosen, but that such a model could not handle semantic interaction. A neural network system is presented that does handle semantic interaction.

Paper Details

Date Published: 2 March 1994
PDF: 8 pages
Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); doi: 10.1117/12.169961
Show Author Affiliations
George L. Dunbar, Warwick Univ. (United Kingdom)

Published in SPIE Proceedings Vol. 2243:
Applications of Artificial Neural Networks V
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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