@inproceedings{hwang-etal-2023-uncertainty,
title = "Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems",
author = "Hwang, Jinha and
Gudumotu, Carol and
Ahmadnia, Benyamin",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.59",
pages = "541--547",
abstract = {This paper addresses the challenge of uncertainty quantification in text classification for medical purposes and provides a three-fold approach to support robust and trustworthy decision-making by medical practitioners. Also, we address the challenge of imbalanced datasets in the medical domain by utilizing the Mondrian Conformal Predictor with a Na{\"\i}ve Bayes classifier.},
}
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%0 Conference Proceedings
%T Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems
%A Hwang, Jinha
%A Gudumotu, Carol
%A Ahmadnia, Benyamin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F hwang-etal-2023-uncertainty
%X This paper addresses the challenge of uncertainty quantification in text classification for medical purposes and provides a three-fold approach to support robust and trustworthy decision-making by medical practitioners. Also, we address the challenge of imbalanced datasets in the medical domain by utilizing the Mondrian Conformal Predictor with a Naïve Bayes classifier.
%U https://aclanthology.org/2023.ranlp-1.59
%P 541-547
Markdown (Informal)
[Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems](https://aclanthology.org/2023.ranlp-1.59) (Hwang et al., RANLP 2023)
ACL