@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://preview.aclanthology.org/jlcl-multiple-ingestion/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.}
}