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.- Anthology ID:
- 2023.semeval-1.42
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 312–317
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.42
- DOI:
- 10.18653/v1/2023.semeval-1.42
- Cite (ACL):
- Lung-Hao Lee, Yuan-Hao Cheng, Jen-Hao Yang, and Kao-Yuan Tien. 2023. NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 312–317, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- NCUEE-NLP at SemEval-2023 Task 8: Identifying Medical Causal Claims and Extracting PIO Frames Using the Transformer Models (Lee et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.semeval-1.42.pdf