Abstract
Given the high-stakes nature of healthcare decision-making, we aim to improve the efficiency of human annotators rather than replacing them with fully automated solutions. We introduce a new comprehensive resource, SYMPTOMIFY, a dataset of annotated vaccine adverse reaction reports detailing individual vaccine reactions. The dataset, consisting of over 800k reports, surpasses previous datasets in size. Notably, it features reasoning-based explanations alongside background knowledge obtained via language model knowledge harvesting. We evaluate performance across various methods and learning paradigms, paving the way for future comparisons and benchmarking.- Anthology ID:
- 2023.findings-emnlp.781
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11667–11681
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.781
- DOI:
- 10.18653/v1/2023.findings-emnlp.781
- Cite (ACL):
- Bosung Kim and Ndapa Nakashole. 2023. SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11667–11681, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting (Kim & Nakashole, Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.findings-emnlp.781.pdf