Dunn, J.; Beltran de Heredia, J.; Burke, M.; Gandy, L.; Kanareykin, S.; Kapah, O.; Taylor, M.; Hines, D.; Frieder, O.; Grossman, D.; Howard, N.; Koppel, M.; Morris, S.; Ortony, A.; & Argamon, S. (2014). “Language-Independent Ensemble Approaches to Metaphor Identification.” In Proceedings of the 28th Conference on Artificial Intelligence: Workshop on Cognitive Computing for Augmented Human Intelligence (AAAI 2014). 6-12.
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 languageindependent 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.