Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning

Hongyi Yuan, Zheng Yuan, Sheng Yu


Abstract
Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.Generative methods achieve remarkable performances in general domain EL with less memory usage while requiring expensive pre-training.Previous biomedical EL methods leverage synonyms from knowledge bases (KB) which is not trivial to inject into a generative method.In this work, we use a generative approach to model biomedical EL and propose to inject synonyms knowledge in it.We propose KB-guided pre-training by constructing synthetic samples with synonyms and definitions from KB and require the model to recover concept names.We also propose synonyms-aware fine-tuning to select concept names for training, and propose decoder prompt and multi-synonyms constrained prefix tree for inference.Our method achieves state-of-the-art results on several biomedical EL tasks without candidate selection which displays the effectiveness of proposed pre-training and fine-tuning strategies. The source code is available at https://github.com/Yuanhy1997/GenBioEL.
Anthology ID:
2022.naacl-main.296
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4038–4048
Language:
URL:
https://aclanthology.org/2022.naacl-main.296
DOI:
10.18653/v1/2022.naacl-main.296
Bibkey:
Cite (ACL):
Hongyi Yuan, Zheng Yuan, and Sheng Yu. 2022. Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4038–4048, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (Yuan et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.296.pdf
Code
 yuanhy1997/genbioel
Data
BC5CDRCOMETA