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        <namePart type="given">Hsien-Chi</namePart>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T CARER: Contextualized Affect Representations for Emotion Recognition
%A Saravia, Elvis
%A Liu, Hsien-Chi Toby
%A Huang, Yen-Hao
%A Wu, Junlin
%A Chen, Yi-Shin
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F saravia-etal-2018-carer
%X 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.
%R 10.18653/v1/D18-1404
%U https://aclanthology.org/D18-1404
%U https://doi.org/10.18653/v1/D18-1404
%P 3687-3697

Markdown (Informal)

[CARER: Contextualized Affect Representations for Emotion Recognition](https://aclanthology.org/D18-1404) (Saravia et al., EMNLP 2018)

• CARER: Contextualized Affect Representations for Emotion Recognition (Saravia et al., EMNLP 2018)

ACL

• Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, Junlin Wu, and Yi-Shin Chen. 2018. CARER: Contextualized Affect Representations for Emotion Recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3687–3697, Brussels, Belgium. Association for Computational Linguistics.