@inproceedings{c3f6dec5a3494bfe81276a12f5f0c397,
title = "NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models",
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.",
author = "Lee, \{Lung Hao\} and Cheng, \{Yuan Hao\} and Yang, \{Jen Hao\} and Tien, \{Kao Yuan\}",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 17th International Workshop on Semantic Evaluation, SemEval 2023, co-located with the 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 ; Conference date: 13-07-2023 Through 14-07-2023",
year = "2023",
doi = "10.18653/v1/2023.semeval-1.42",
language = "???core.languages.en\_GB???",
series = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
publisher = "Association for Computational Linguistics",
pages = "312--317",
editor = "Ojha, \{Atul Kr.\} and Dogruoz, \{A. Seza\} and \{Da San Martino\}, Giovanni and Madabushi, \{Harish Tayyar\} and Ritesh Kumar and Elisa Sartori",
booktitle = "17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop",
}