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--- |
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language: |
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- en |
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tags: |
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- biomedical |
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- bionlp |
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- entity linking |
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- embedding |
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- bert |
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--- |
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The GEBERT model pre-trained with GAT graph encoder. |
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The model was published at [CLEF 2023 conference](https://clef2023.clef-initiative.eu/). The source code is available at [github](https://github.com/Andoree/GEBERT). |
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Pretraining data: biomedical concept graph and concept names from the UMLS (2020AB release). |
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Base model: [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext). |
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```bibtex |
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@inproceedings{sakhovskiy2023gebert, |
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author="Sakhovskiy, Andrey |
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and Semenova, Natalia |
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and Kadurin, Artur |
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and Tutubalina, Elena", |
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title="Graph-Enriched Biomedical Entity Representation Transformer", |
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booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction", |
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year="2023", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="109--120", |
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isbn="978-3-031-42448-9" |
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} |
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``` |
