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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The author gratefully acknowledges Arthur Graesser, Jim Hoeffner, and three anonymous reviewers for very helpful comments on earlier versions of this paper and Arthur Graesser for valuable discussions related to the study. Thanks to Phil Goodman for his help with the software and for his kind permission to use material from the manual and program in this article.
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Wiemer-Hastings, K. Abstract noun classification: using a neural network to match word context and word meaning. Behavior Research Methods, Instruments, & Computers 30, 264–271 (1998). https://doi.org/10.3758/BF03200654
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DOI: https://doi.org/10.3758/BF03200654