Abstract
The purpose of this study is to construct a computational model of the metaphor understanding process. This study assumes that metaphor understanding consists of two processes. The first is a categorization process; a target is assigned to an ad hoc category of which the vehicle is a prototypical member. The second is a dynamic interaction process; the target assigned to the ad hoc category is influenced by dynamic interaction among features. Feature emergence is extracted through this dynamic interaction. In this study, a model of metaphor understanding is constructed based on this assumption by applying a statistical analysis of large-scale corpus. Further a psychological experiment is conducted in order to verify the psychological validity of the constructed model of metaphor understanding. Reflecting the fact that the constructed model represents more appropriate features of a metaphor than a model incorporating only the categorization process, the experimental results support its validity.
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Terai, A., Nakagawa, M. (2007). A Computational Model of Metaphor Understanding Consisting of Two Processes. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_98
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DOI: https://doi.org/10.1007/978-3-540-74695-9_98
Publisher Name: Springer, Berlin, Heidelberg
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