|
--- |
|
language: |
|
- en |
|
tags: |
|
- biomedical |
|
- bionlp |
|
- entity linking |
|
- embedding |
|
- bert |
|
--- |
|
The GEBERT model pre-trained with GAT graph encoder. |
|
|
|
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). |
|
|
|
|
|
Pretraining data: biomedical concept graph and concept names from the UMLS (2020AB release). |
|
|
|
Base model: [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co./microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext). |
|
|
|
|
|
|
|
```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" |
|
} |
|
``` |
