EMNLP 2021main1 citations
Guilt by Association: Emotion Intensities in Lexical Representations
Abstract
What do linguistic models reveal about the emotions associated with words? In this study, we consider the task of estimating word-level emotion intensity scores for specific emotions, exploring unsupervised, supervised, and finally a self-supervised method of extracting emotional associations from pretrained vectors and models. Overall, we find that linguistic models carry substantial potential for inducing fine-grained emotion intensity scores, showing a far higher correlation with human ground truth ratings than state-of-the-art emotion lexicons based on labeled data.
BibTeX
@inproceedings{raji-de-melo-2021-guilt,
title = "Guilt by Association: Emotion Intensities in Lexical Representations",
author = "Raji, Shahab and
de Melo, Gerard",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.781/",
doi = "10.18653/v1/2021.emnlp-main.781",
pages = "9911--9917"
}