A neural network model of metaphor understanding with dynamic interaction based on a statistical language analysis: Targeting a human-like model

The purpose of this paper is to construct a model that represents the human process of understanding metaphors, focusing specifically on similes of the form an '' A like B ''. Generally speaking, human beings are able to generate and understand many sorts of metaphors. This study constructs the model based on a probabilistic knowledge structure for concepts which is computed from a statistical analysis of a large-scale corpus. Consequently, this model is able to cover the many kinds of metaphors that human beings can generate. Moreover, the model implements the dynamic process of metaphor understanding by using a neural network with dynamic interactions. Finally, the validity of the model is confirmed by comparing model simulations with the results from a psychological experiment.