---
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}
}
```