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CARER: Contextualized Affect Representations for Emotion Recognition
lib:0917382142308932 (v1.0.0)
Authors: Elvis Saravia,Hsien-Chi Toby Liu,Yen-Hao Huang,Junlin Wu,Yi-Shin Chen
Where published: EMNLP 2018 10
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/D18-1404/
Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.
Where published: EMNLP 2018 10
Document: PDF DOI
Abstract URL: https://www.aclweb.org/anthology/D18-1404/
Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.
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