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        <title>Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems</title>
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    <name type="personal">
        <namePart type="given">Jinha</namePart>
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    <name type="personal">
        <namePart type="given">Benyamin</namePart>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
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    <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 Naïve Bayes classifier.</abstract>
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        <url>https://aclanthology.org/2023.ranlp-1.59</url>
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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)

• Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems (Hwang et al., RANLP 2023)

ACL

• Jinha Hwang, Carol Gudumotu, and Benyamin Ahmadnia. 2023. Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 541–547, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.