Biomedical Entity Linking as Multiple Choice Question Answering

• URL: http://arxiv.org/abs/2402.15189v2
• Date: Fri, 17 May 2024 09:11:44 GMT
• Title: Biomedical Entity Linking as Multiple Choice Question Answering
• Authors: Zhenxi Lin, Ziheng Zhang, Xian Wu, Yefeng Zheng,
• Abstract summary: We present BioELQA, a novel model that treats Biomedical Entity Linking as Multiple Choice Question Answering. We 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. To improve generalization for long-tailed entities, we retrieve similar labeled training instances as clues and the input with retrieved instances for the generator.
• Score: 48.74212158495695
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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