Analysing aspects of how our brain processes language may provide, even before the language faculty is really understood, useful insights into higher order cognitive functions. We have taken initial steps in this direction, focusing on the mass-count distinction. The mass-count distinction relates to the countability or un-countability of nouns in terms of their syntactic usage. Our first results show that the mass-count distinction, across a number of natural languages, is far from bimodal, and exhibits in fact complex fuzzy relations between syntax and semantics. We then tried to test the ability of a standard, biologically plausible self-organising neural network to learn such associations between syntax and semantics. A neural network that expresses competition amongst output neurons with lateral inhibition can identify the basic classes of mass and count in the syntactic markers and produce a graded distribution of the nouns along the mass-count spectrum. The network however fails to successfully map the semantic classes of the nouns to their syntactic usage, thus corroborating the hypothesis that the syntactic usage of nouns in the mass-count domain is not simply predicted by the semantics of the noun.
|Title of host publication
|Mass and Count in Linguistics, Philosophy, and Cognitive Science
|John Benjamins Publishing Company
|Number of pages
|Published - 2020
|Language Faculty and Beyond
Bibliographical notePublisher Copyright:
© 2020 John Benjamins Publishing Company.
- Mass-count distinction
- Neural networks
- Syntax-semantics interaction