ABSTRACT

Interpreting metaphors is an integral and inescapable process in human understanding of natural language. Part I of this paper discusses a method of analyzing metaphors based on the existence of a small number of generalized metaphor mappings. Each generalized metaphor contains a recognition network, a basic mapping, additional transfer mappings, and an implicit intention component. It is argued that the method reduces metaphor interpretation from a reconstruction to a recognition task. Implications towards automating certain aspects of language learning are also discussed. Part II analyzes the analogical mappings underlying metaphors and the implications for inference and memory organization. The central thesis is that human inference processes are governed by the same analogical mappings manifest as metaphors in language.