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.- Anthology ID:
- 2023.ranlp-1.59
- Volume:
- Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 541–547
- Language:
- URL:
- https://aclanthology.org/2023.ranlp-1.59
- DOI:
- Cite (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.
- Cite (Informal):
- Uncertainty Quantification of Text Classification in a Multi-Label Setting for Risk-Sensitive Systems (Hwang et al., RANLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.ranlp-1.59.pdf