Comprehending and Generating Apt Metaphors: A Web-driven, Case-based Approach to Figurative Language

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 challenging 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 (nonexplicit metaphors). We demonstrate that the markedness of similes allows for a large case-base of illustrative examples to be easily acquired from the web, and present a system, called Sardonicus, that uses this casebase both to understand property-attribution metaphors and to generate apt metaphors for a given target on demand. In each case, we show how the text of the web is used as a source of tacit knowledge about what categorizations are allowable and what properties are most contextually appropriate. Overall, we demonstrate that by using the web as a primary knowledge source, a system can achieve a robust and scalable competence with metaphor while minimizing the need for hand-crafted resources like WordNet.