Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Hongyi Yuan, Zheng Peng Yuan, Sheng Yu · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 KBguided 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 multisynonyms 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 Github.com/Yuanhy1997/GenBioEL.