AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Language-Independent Ensemble Approaches to Metaphor Identification
Jonathan Dunn, Jon Beltran de Heredia, Maura Burke, Lisa Gandy, Sergey Kanareykin, Oren Kapah, Matthew Taylor, Dell Hines, Ophir Frieder, David Grossman, Newton Howard, Moshe Koppel, Scott Morris, Andrew Ortony, Shlomo Argamon

Last modified: 2014-06-18

Abstract


True natural language understanding requires the ability to identify and understand metaphorical utterances, which are ubiquitous in human communication of all kinds. At present, however, even the problem of identifying metaphors in arbitrary text is very much an unsolved problem, let alone analyzing their meaning. Furthermore, no current methods can be transferred to new languages without the development of extensive language-specific knowledge bases and similar semantic resources. In this paper, we present a new language-independent ensemble-based approach to identifying linguistic metaphors in natural language text. The system's architecture runs multiple corpus-based metaphor identification algorithms in parallel and combines their results. The architecture allows easy integration of new metaphor identification schemes as they are developed. This new approach achieves state-of-the-art results over multiple languages and represents a significant improvement over existing methods for this problem.

Keywords


metaphor, conceptual metaphor

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