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

Anonymous

Paper Link: https://openreview.net/forum?id=k48c4h2m8Ue

Paper Type: Short paper (up to four pages of content + unlimited references and appendices)

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 \url{https://github.com/Yuanhy1997/GenBioEL}.

Copyright Consent Signature (type Name Or NA If Not Transferrable): Zheng Yuan

Copyright Consent Name And Address: Zheng Yuan, Beijing