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

TL;DR

This paper introduces a generative biomedical entity linking method that effectively incorporates knowledge base synonyms through KB-guided pre-training and synonyms-aware fine-tuning, achieving state-of-the-art results.

Contribution

It presents a novel approach to inject knowledge base synonyms into generative models for biomedical entity linking, improving performance without candidate selection.

Findings

Achieves state-of-the-art results on biomedical EL tasks.

Effectively incorporates KB synonyms into generative models.

Eliminates the need for candidate selection in EL.

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.