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Neural Metaphor Detection in Context

2018-08-29 · EMNLP 2018 10 · Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer

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.

📄 PDF Abstract BibTeX arXiv:1808.09653

Code (1)

gao-g/metaphor-in-context 공식 구현 pytorch

Methods 이 논문이 사용한 방법론

Sigmoid Activation 설명 없음
Tanh Activation 설명 없음
LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…
BiLSTM A Bidirectional LSTM, or biLSTM, is a sequence processing model that consists of two LSTMs: one taking the input in a forward direction, and the other in a backwards…

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