BioFEG: Generate Latent Features for Biomedical Entity Linking (2023.emnlp-main)
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| Challenge: | Existing approaches to biomedical entity linking suffer from multiple types of errors due to the rarity of many biomedically relevant entities in real-world scenarios. |
| Approach: | They propose a latent feature generation framework to generate latent semantic features for unseen entities to capture fine-grained coherence information of unseened entities. |
| Outcome: | The proposed framework is superior to existing models on two benchmark datasets. |
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| Challenge: | Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective. |
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Prasanth Bathala, Christophe Ye, Batuhan Nursal, Shubham Lohiya, David Kartchner, Cassie S. Mitchell
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David Kartchner, Jennifer Deng, Shubham Lohiya, Tejasri Kopparthi, Prasanth Bathala, Daniel Domingo-Fernández, Cassie Mitchell
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
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BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks (2021.findings-emnlp)
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| Challenge: | Biomedical entity linking is a task of linking entities in biomedical documents to referent entities in a knowledge base. |
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LLM as Entity Disambiguator for Biomedical Entity-Linking (2025.acl-short)
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