Neural net models of word representation : a connectionist approach to word meaning and lexical relations
This study examines the use of the neural net paradigm as a modeling tool to represent word meanings. The neural net paradigm, also called "connectionism" and "parallel distributed processing," provides a new metaphor and vocabulary for representing the structure of the mental le...
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2011
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| Online Access: | http://cardinalscholar.bsu.edu/handle/handle/179016 http://liblink.bsu.edu/uhtbin/catkey/832999 |
| Summary: | This study examines the use of the neural net paradigm as a modeling tool to represent word meanings. The neural net paradigm, also called "connectionism" and "parallel distributed processing," provides a new metaphor and vocabulary for representing the structure of the mental lexicon. As a research method applied to the componential analysis of word meanings, the neural net approach has one primary advantage over the traditional introspective method: freedom from the investigator's personal biases.The connectionist method is illustrated in this thesis with an extensive examination of the meanings of the words "cup" and "mug." These words have been studied previously by Labov (1973), Wierzbicka (1985), Andersen (1975), and Kempton (1978), using very different methods.The neural net models developed in this study are based on empirical data acquired through interviews with nine informants who classified 37 objects, 37 photographs, and 37 line drawings as "cups," "mugs," or "neither." These responses were combined with a data file representing the coded attributes of each object, to construct neural net models which reflect each informant's classification process.In the neural net models, the "cup" and "mug" features are interconnected with positive and negative weights that represent the association strengths of the features. When the connection weights are set so that they reflect the informants' responses, the neural net models can account for the extreme discrepancies in object-naming among informants, and the models can also account for the inconsistent classifications of each individual informant with respect to the mode of presentation (drawing, photograph, or actual object). Further, the neural net modelscan predict classifications for novel objects with an accuracy varying from 82% to 100%.By examining the connection weight patterns within the neural net model, it is possible to discover the "cup" and "mug" features which are most salient for each informant, and for the informants collectively. This analysis shows that each informant has acquired internal meanings for the words "cup" and "mug" which are unique to the individual, although there is considerable overlap with respect to the most salient features. === Department of English |
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