Abstract
Psychologists have used artificial neural networks for a few decades to simulate perception, language acquisition, and other cognitive processes. This paper discusses the use of artificial neural networks in research on semantics—in particular, in the investigation of abstract noun meanings. It is widely acknowledged that a word’s meaning varies with its contexts of use, but it is a complex task to identify which context elements are relevant to a word’s meaning. The present study illustrates how connectionist networks can be used to examine this problem. A simple feedforward network learned to distinguish among six abstract nouns, on the basis of characteristics of their contexts, in a corpus of randomly selected naturalistic sentences.
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