Abstract
The use of figurative language is ubiquitous in natural language texts and it is a serious bottleneck in automatic text understanding. A system capable of interpreting figurative expressions would be an invaluable addition to the real-world natural language processing (NLP) applications that need to access semantics, such as machine translation, opinion mining, question answering and many others. In this article we focus on one type of figurative language, logical metonymy, and present a computational model of its interpretation bringing together statistical techniques and the insights from linguistic theory. Compared to previous approaches this model is both more informative and more accurate. The system produces sense-level interpretations of metonymic phrases and then automatically organizes them into conceptual classes, or roles, discussed in the majority of linguistic literature on the phenomenon.
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