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Neural Metaphor Detection in Context

2018-08-29EMNLP 2018Code Available0· sign in to hype

Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer

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Abstract

We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in this setting, in comparison to previous work that used more restricted forms of linguistic context. These models establish a new state-of-the-art on existing verb metaphor detection benchmarks, and show strong performance on jointly predicting the metaphoricity of all words in a running text.

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