EMNLP 2023long findings0 citations
SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting
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.
Symptom Recognition
BibTeX
@inproceedings{
kim2023symptomify,
title={{SYMPTOMIFY}: Transforming Symptom Annotations with Language Model Knowledge Harvesting},
author={Bosung Kim and Ndapa Nakashole},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=HKMvR1UaWH}
}