Biomedical Entity Linking as Multiple Choice Question Answering
<|reference_start|>Biomedical Entity Linking as Multiple Choice Question Answering: 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.<|reference_end|>
@article{lin2024biomedical,
    title={Biomedical Entity Linking as Multiple Choice Question Answering},
    author={Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng},
    journal={arXiv preprint arXiv:2402.15189},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15189},
    primaryClass={cs.CL cs.AI cs.LG}
}