Abstract
We study the problem of entity detection and normalization applied to patient self-reports of symptoms that arise as side-effects of vaccines. Our application domain presents unique challenges that render traditional classification methods ineffective: the number of entity types is large; and many symptoms are rare, resulting in a long-tail distribution of training examples per entity type. We tackle these challenges with an autoregressive model that generates standardized names of symptoms. We introduce a data augmentation technique to increase the number of training examples for rare symptoms. Experiments on real-life patient vaccine symptom self-reports show that our approach outperforms strong baselines, and that additional examples improve performance on the long-tail entities.- Anthology ID:
- 2022.bionlp-1.29
- Volume:
- Proceedings of the 21st Workshop on Biomedical Language Processing
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 310–315
- Language:
- URL:
- https://aclanthology.org/2022.bionlp-1.29
- DOI:
- 10.18653/v1/2022.bionlp-1.29
- Cite (ACL):
- Bosung Kim and Ndapa Nakashole. 2022. Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 310–315, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Data Augmentation for Rare Symptoms in Vaccine Side-Effect Detection (Kim & Nakashole, BioNLP 2022)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2022.bionlp-1.29.pdf