Puer at SemEval-2024 Task 2: A BioLinkBERT Approach to Biomedical Natural Language Inference
Jiaxu Dao, Zhuoying Li, Xiuzhong Tang, Xiaoli Lan, Junde Wang
Abstract
This paper delineates our investigation into the application of BioLinkBERT for enhancing clinical trials, presented at SemEval-2024 Task 2. Centering on the medical biomedical NLI task, our approach utilized the BioLinkBERT-large model, refined with a pioneering mixed loss function that amalgamates contrastive learning and cross-entropy loss. This methodology demonstrably surpassed the established benchmark, securing an impressive F1 score of 0.72 and positioning our work prominently in the field. Additionally, we conducted a comparative analysis of various deep learning architectures, including BERT, ALBERT, and XLM-RoBERTa, within the context of medical text mining. The findings not only showcase our method’s superior performance but also chart a course for future research in biomedical data processing. Our experiment source code is available on GitHub at: https://github.com/daojiaxu/semeval2024_task2.- Anthology ID:
- 2024.semeval-1.12
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 70–75
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.12
- DOI:
- Cite (ACL):
- Jiaxu Dao, Zhuoying Li, Xiuzhong Tang, Xiaoli Lan, and Junde Wang. 2024. Puer at SemEval-2024 Task 2: A BioLinkBERT Approach to Biomedical Natural Language Inference. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 70–75, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Puer at SemEval-2024 Task 2: A BioLinkBERT Approach to Biomedical Natural Language Inference (Dao et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.12.pdf