| --- |
| language: |
| - en |
| tags: |
| - biomedical |
| - bionlp |
| - entity linking |
| - embedding |
| - bert |
| --- |
| 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 |
| @inproceedings{sakhovskiy2023gebert, |
| author="Sakhovskiy, Andrey |
| and Semenova, Natalia |
| and Kadurin, Artur |
| and Tutubalina, Elena", |
| title="Graph-Enriched Biomedical Entity Representation Transformer", |
| booktitle="Experimental IR Meets Multilinguality, Multimodality, and Interaction", |
| year="2023", |
| publisher="Springer Nature Switzerland", |
| address="Cham", |
| pages="109--120", |
| isbn="978-3-031-42448-9" |
| } |
| ``` |
