mlsanityData to 7 Oct 2026

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text

arXiv 2110.08228 ↗First posted 15 Oct 2021cs.CL cs.AIcited by 15

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Abstract (arXiv)

Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 15 Oct 2021.

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15 of 24 references.

References · 15

  1. 2020Self-Alignment Pretraining for Biomedical Entity Representationscited by 233
  2. 2019Zero-Shot Entity Linking by Reading Entity Descriptionscited by 124
  3. 2021Robustness Gym: Unifying the NLP Evaluation Landscapecited by 107
  4. 2019Scalable Zero-shot Entity Linking with Dense Entity Retrievalcited by 85
  5. 2019MedMentions: A Large Biomedical Corpus Annotated with UMLS Conceptscited by 79
  6. 2021Improving Biomedical Pretrained Language Models with Knowledgecited by 36
  7. 2020Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognitioncited by 35
  8. 2020Explaining and Improving Model Behavior with k Nearest Neighbor Representationscited by 27
  9. 2020Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguationcited by 24
  10. 2021Fast and Effective Biomedical Entity Linking Using a Dual Encodercited by 16
  11. 2018Ontology Alignment in the Biomedical Domain Using Entity Definitions and Contextcited by 10
  12. 2020Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasetscited by 9
3 more
  1. 2020Robust Benchmarking for Machine Learning of Clinical Entity Extractioncited by 3
  2. 2020WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Rankingcited by 2
  3. 2020Clustering-based Inference for Biomedical Entity Linkingcited by 2

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Citing papers per year: 2022: 3; 2023: 6; 2024: 5; 2025: 0; 2026: 1.

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  1. 2022Domino: Discovering Systematic Errors with Cross-Modal Embeddingscited by 155
  2. 2022BioBART: Pretraining and Evaluation of A Biomedical Generative Language Modelcited by 83
  3. 2022Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuningcited by 34
  4. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  5. 2024HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization toolscited by 8
  6. 2023BELB: a Biomedical Entity Linking Benchmarkcited by 7
  7. 2024Multi-group Learning for Hierarchical Groupscited by 7
  8. 2024BELHD: Improving Biomedical Entity Linking with Homonoym Disambiguationcited by 4
  9. 2023Proxy-based Zero-Shot Entity Linking by Effective Candidate Retrievalcited by 2
  10. 2023Exploring Partial Knowledge Base Inference in Biomedical Entity Linkingcited by 2
  11. 2023Biomedical Entity Linking with Triple-aware Pre-Trainingcited by 2
  12. 2024Biomedical Entity Linking as Multiple Choice Question Answeringcited by 2
3 more
  1. 2026BeLink: Biomedical Entity Linking Meets Generative Re-Rankingcited by 1
  2. 2023Iteratively Improving Biomedical Entity Linking and Event Extraction via Hard Expectation-Maximizationcited by 0
  3. 2024Boosting Biomedical Concept Extraction by Rule-Based Data Augmentationcited by 0