Proceedings of the Annual Meeting of the Cognitive Science Society

This paper explores the use of an artificial neural network to investigate the mental representation of abstract noun meanings. Unlike concrete nouns, abstract nouns refer to entities that cannot be pointed to. Cues to their meaning must therefore be in their context of use. It has frequently been shown that the meaning of a word varies with its contexts of use. It is more difficult, however, to identify which elements of context are relevant to a word's meaning. The present study demonstrates that a connectionist network can be used to examine this problem. A feedforward network learned to distinguish among seven abstract nouns based on characteristics of their verbal contexts in a corpus of randomly selected sentences. The results suggest that, for our sample, the contextual representation of abstract nouns is in principle sufficient to identify and distinguish abstract nouns and thus meets the functional requirements of concept representation.