@inproceedings{kim-nakashole-2023-symptomify,
title = "{SYMPTOMIFY}: Transforming Symptom Annotations with Language Model Knowledge Harvesting",
author = "Kim, Bosung and
Nakashole, Ndapa",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.781",
doi = "10.18653/v1/2023.findings-emnlp.781",
pages = "11667--11681",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting
%A Kim, Bosung
%A Nakashole, Ndapa
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kim-nakashole-2023-symptomify
%X 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.
%R 10.18653/v1/2023.findings-emnlp.781
%U https://aclanthology.org/2023.findings-emnlp.781
%U https://doi.org/10.18653/v1/2023.findings-emnlp.781
%P 11667-11681
Markdown (Informal)
[SYMPTOMIFY: Transforming Symptom Annotations with Language Model Knowledge Harvesting](https://aclanthology.org/2023.findings-emnlp.781) (Kim & Nakashole, Findings 2023)
ACL