Biomedical Entity Linking as Multiple Choice Question Answering
Zhenxi Lin Ziheng Zhang Xian Wu Yefeng Zheng
Although biomedical entity linking (BioEL) has made significant progress with
pre-trained language models, challenges still exist for fine-grained and
long-tailed entities. To address these challenges, we present BioELQA, a novel
model that treats Biomedical Entity Linking as Multiple Choice Question
Answering. BioELQA first obtains candidate entities with a fast retriever,
jointly presents the mention and candidate entities to a generator, and then
outputs the predicted symbol associated with its chosen entity. This
formulation enables explicit comparison of different candidate entities, thus
capturing fine-grained interactions between mentions and entities, as well as
among entities themselves. To improve generalization for long-tailed entities,
we retrieve similar labeled training instances as clues and concatenate the
input with retrieved instances for the generator. Extensive experimental
results show that BioELQA outperforms state-of-the-art baselines on several
datasets.