Papers with biomedical entity linking
BENNERD: A Neural Named Entity Linking System for COVID-19 (2020.emnlp-demos)
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| Challenge: | a biomedical entity linking system is available for COVID-19 research. |
| Approach: | They propose a biomedical entity linking system that detects named enti- ties in text and links them to the UMLS knowledge base. |
| Outcome: | The proposed system detects named enti- ties in text and links them to the unified medical language system (UMS) knowledge base entries. |
Knowledge-Rich Self-Supervision for Biomedical Entity Linking (2022.findings-emnlp)
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Sheng Zhang, Hao Cheng, Shikhar Vashishth, Cliff Wong, Jinfeng Xiao, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, Hoifung Poon
| Challenge: | Entity linking is challenging in high-value domains with myriad entities . standard classification approaches suffer from the annotation bottleneck . |
| Approach: | They propose a self-supervised approach to learn domain knowledge for biomedical entity linking . it generates self-reported mention examples on unlabeled text and trains contextual encoder . |
| Outcome: | The proposed method outperforms existing methods by 20 points in accuracy on biomedical datasets. |
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking (2021.acl-short)
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| Challenge: | Existing work on transferring domain-specific knowledge from a pretraining model to a resource-poor language is limited to English . a novel cross-lingual biomedical entity linking task is proposed to improve this capability. |
| Approach: | They propose a cross-lingual biomedical entity linking task and establish a new benchmark spanning 10 typologically diverse languages. |
| Outcome: | The proposed methods yield consistent gains across all target languages, sometimes up to 20 Precision@1 points, without any in-domain knowledge in the target language and without any parallel data. |
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. |
| Approach: | They propose an efficient convolutional neural network with residual connections for biomedical entity linking. |
| Outcome: | The proposed model achieves comparable or even better linking accuracy on five public datasets while having about 60 times fewer parameters. |
Biomedical Entity Linking as Multiple Choice Question Answering (2024.lrec-main)
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| Challenge: | Existing methods for biomedical entity linking are discriminative and disambiguative . Existing models for bioMEDical entity linking use a BERT-based encoder to encode mentions and entities into the same embedding space and dissociate mentions by nearest neighbors. |
| Approach: | They propose a model that treats biomedical entity linking as Multiple Choice Question Answering. |
| Outcome: | The proposed model outperforms state-of-the-art models on several datasets. |
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)
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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. |
| Approach: | They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names. |
| Outcome: | The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection. |
Learning from Negative Samples in Biomedical Generative Entity Linking (2025.findings-acl)
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| Challenge: | Generative models are usually trained only with positive samples and do not explicitly learn from hard negative samples, which are entities that look similar but have different meanings. |
| Approach: | They propose a framework that trains generative BioEL models using negative samples to learn from hard negative samples. |
| Outcome: | The proposed framework outperforms baseline models by up to an average top-1 accuracy of 1.4% on five benchmarks. |