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---
language:
- en
bigbio_language:
- English
license: cc-by-sa-4.0
multilinguality: monolingual
bigbio_license_shortname: CC_BY_SA_4p0
pretty_name: BEAR
homepage: https://www.ims.uni-stuttgart.de/en/research/resources/corpora/bioclaim/
bigbio_pubmed: False
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- RELATION_EXTRACTION
---
# Dataset Card for BEAR
## Dataset Description
- **Homepage:** https://www.ims.uni-stuttgart.de/en/research/resources/corpora/bioclaim/
- **Pubmed:** False
- **Public:** True
- **Tasks:** NER, RE
A dataset of 2100 Twitter posts annotated with 14 different types of biomedical entities (e.g., disease, treatment,
risk factor, etc.) and 20 relation types (including caused, treated, worsens, etc.).
## Citation Information
```
@InProceedings{wuehrl_klinger_2022,
author = {Wuehrl, Amelie and Klinger, Roman},
title = {Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)},
booktitle = {Proceedings of The 13th Language Resources and Evaluation Conference},
month = {June},
year = {2022},
address = {Marseille, France},
publisher = {European Language Resources Association}
}
```
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