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Interpreting metaphors is an integral and inescapable process in human understanding of natural language. 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. | Metaphor - A Key to Extensible Semantic Analysis Jaime G. Carbonell Carnegie-Mellon University Pittsburgh PA 15213 Abstract Interpreting metaphors is an integral and inescapable process in human understanding of natural language. 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.1 1. An Opening Argument A dream of many computational linguists is to produce a natural language analyzer that tries its best to process language that almost but not quite corresponds to the system s grammar dictionary and semantic knowledge base. In addition some of US envision a language analyzer that improves its performance with experience. To these ends. I developed the protect and integrate algorithm a method of inducing possible meanings of unknown words from context and storing the new information for eventual addition to the dictionary 1 . While useful this mechanism addresses only one aspect of the larger problem accruing certain classes of word definitions in the dictionary. In this paper I focus on the problem of augmenting the power of a semantic knowledge base used for language analysis by means of metaphorical mappings. The pervasiveness of metaphor in every aspect of human communication has been convincingly demonstrated by Lakoff and Johnson 4 Ortony 6 Hobbs 3 and many others. However the creation of a process model to encompass metaphor comprehension has not been of central concern.2 From a computational standpoint metaphor has been viewed as an obstacle to be tolerated at best and ignored at worst. For instance Wilks 9 gives a few rules on how to relax semantic .
