@inproceedings{lee-etal-2023-ncuee,
title = "{NCUEE}-{NLP} at {S}em{E}val-2023 Task 8: Identifying Medical Causal Claims and Extracting {PIO} Frames Using the Transformer Models",
author = "Lee, Lung-Hao and
Cheng, Yuan-Hao and
Yang, Jen-Hao and
Tien, Kao-Yuan",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.42",
doi = "10.18653/v1/2023.semeval-1.42",
pages = "312--317",
abstract = "This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15{\%}. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78{\%} for Population class, 43.58{\%} for Intervention class, and 30.67{\%} for Outcome class, resulting in a macro-averaging F1-score of 37.34{\%}. Our system evaluation results ranked second position among all participating teams.",
}
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<abstract>This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15%. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78% for Population class, 43.58% for Intervention class, and 30.67% for Outcome class, resulting in a macro-averaging F1-score of 37.34%. Our system evaluation results ranked second position among all participating teams.</abstract>
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%0 Conference Proceedings
%T NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models
%A Lee, Lung-Hao
%A Cheng, Yuan-Hao
%A Yang, Jen-Hao
%A Tien, Kao-Yuan
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F lee-etal-2023-ncuee
%X This study describes the model design of the NCUEE-NLP system for the SemEval-2023 Task 8. We use the pre-trained transformer models and fine-tune the task datasets to identify medical causal claims and extract population, intervention, and outcome elements in a Reddit post when a claim is given. Our best system submission for the causal claim identification subtask achieved a F1-score of 70.15%. Our best submission for the PIO frame extraction subtask achieved F1-scores of 37.78% for Population class, 43.58% for Intervention class, and 30.67% for Outcome class, resulting in a macro-averaging F1-score of 37.34%. Our system evaluation results ranked second position among all participating teams.
%R 10.18653/v1/2023.semeval-1.42
%U https://aclanthology.org/2023.semeval-1.42
%U https://doi.org/10.18653/v1/2023.semeval-1.42
%P 312-317
Markdown (Informal)
[NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models](https://aclanthology.org/2023.semeval-1.42) (Lee et al., SemEval 2023)
ACL