Finding structure in figurative language: Metaphor detection with topic-based frames

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Abstract

In this paper, we present a novel and highly effective method for induction and application of metaphor frame templates as a step toward detecting metaphor in extended discourse. We infer implicit facets of a given metaphor frame using a semi-supervised bootstrapping approach on an unlabeled corpus. Our model applies this frame facet information to metaphor detection, and achieves the state-of-the-art performance on a social media dataset when building upon other proven features in a nonlinear machine learning model. In addition, we illustrate the mechanism through which the frame and topic information enable the more accurate metaphor detection.

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Jang, H., Maki, K., Hovy, E., & Rosé, C. P. (2017). Finding structure in figurative language: Metaphor detection with topic-based frames. In SIGDIAL 2017 - 18th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference (pp. 320–330). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w17-5538

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