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.
Cite
CITATION STYLE
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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