@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",
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",
}