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Learning Outside the Box: Discourse-level Features Improve Metaphor Identification.

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Peer-reviewed

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Authors

Mu, Jesse 
Giannakoudaki, Eleni 
Shutova, Ekaterina 

Abstract

Most current approaches to metaphor identi-fication use restricted linguistic contexts, e.g.by considering only a verb’s arguments or the sentence containing a phrase.Inspired by pragmatic accounts of metaphor, we argue that broader discourse features are crucial for bet-ter metaphor identification. We train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods, obtaining near state-of-the-art results on the 2018 VU Amsterdam metaphor iden-tification task without the complex metaphor-specific features or deep neural architectures employed by other systems.A qualitative analysis further confirms the need for broader context in metaphor processing.

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In Proceedings of the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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17th Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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Cambridge Assessment (unknown)