Abstract
The purpose of this study is to construct a human-like neural network model that represents the process of metaphor understanding with dynamic interaction, based on data obtained from statistical language analysis. In this paper, the probabilistic relationships between concepts and their attribute values are first computed from the statistical analysis of language data. Secondly, a computational model of the metaphor understanding process is constructed, including dynamic interaction among attribute values. Finally, a psychological experiment is conducted to examine the psychological validity of the model.
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Terai, A., Nakagawa, M. (2006). A Neural Network Model of Metaphor Understanding with Dynamic Interaction Based on a Statistical Language Analysis. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_52
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DOI: https://doi.org/10.1007/11840817_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38625-4
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