| 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} | |
| } | |
| ``` | |