Comprehending and generating apt metaphors: a web-driven, case-based approach to figurative language
Tony Veale, Yanfen Hao · 2007
Examples of figurative language can range from the explicit and the obvious to the implicit and downright enigmatic. Some simpler forms, like simile, often wear their meanings on their sleeve, while more challeng-ing forms, like metaphor, can make cryptic allusions more akin to those of riddles or crossword puzzles. In this paper we argue that because the same concepts and properties are described in either case, a computational agent can learn from the easy cases (explicit similes) how to comprehend and generate the hard cases (non-explicit metaphors). We demonstrate that the marked-ness of similes allows for a large case-base of illustra-tive examples to be easily acquired from the web, and present a system, called Sardonicus, that uses this case-base both to understand property-attribution metaphors and to generate apt metaphors for a given target on de-mand. In each case, we show how the text of the web is used as a source of tacit knowledge about what cate-gorizations are allowable and what properties are most contextually appropriate. Overall, we demonstrate that by using the web as a primary knowledge source, a sys-tem can achieve a robust and scalable competence with metaphor while minimizing the need for hand-crafted resources like WordNet.