CARER: Contextualized Affect Representations for Emotion Recognition (original) (raw)
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.
Anthology ID:
D18-1404
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff,David Chiang,Julia Hockenmaier,Jun’ichi Tsujii
Venue:
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3687–3697
Language:
URL:
https://aclanthology.org/D18-1404/
DOI:
Bibkey:
Cite (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.
Cite (Informal):
CARER: Contextualized Affect Representations for Emotion Recognition (Saravia et al., EMNLP 2018)
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https://aclanthology.org/D18-1404.pdf
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