A corpus-based computational model of metaphor understanding consisting of two processes

The purpose of this study is to construct a computational model of metaphor understanding based on statistical corpora analysis and that includes dynamic interaction among features. The constructed model consists of two processes: a categorization process and a dynamic-interaction process. The categorization process model, which is based on the class inclusion theory, represents how a target is assigned to an ad hoc category of which the vehicle is a prototypical member. The dynamic-interaction process model represents how the target assigned to the ad hoc category is influenced and how emergent features are emphasized by dynamic interactions among features. The dynamic interaction is realized based on a recurrent neural network. The constructed model is able to highlight the emphasized features of a metaphorical expression. Finally, real-world experiments are conducted in order to verify the semantic validity of the constructed model of metaphor understanding with dynamic interactions. The results from the real-world experiments support the model incorporating dynamic interaction.