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A Neural Network Model of Metaphor Understanding with Dynamic Interaction Based on a Statistical Language Analysis

  • Asuka Terai
  • Masanori Nakagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

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.

Keywords

Conditional Probability Neural Network Model Dynamic Interaction Latent Semantic Analysis Psychological Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Asuka Terai
    • 1
  • Masanori Nakagawa
    • 1
  1. 1.Tokyo Institute of TechnologyTokyoJapan

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