We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives


Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!