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---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: NCBI_LICENSE
pretty_name: GENETAG
homepage: https://github.com/openbiocorpora/genetag
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for GENETAG
## Dataset Description
- **Homepage:** https://github.com/openbiocorpora/genetag
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER
Named entity recognition (NER) is an important first step for text mining the biomedical literature.
Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus.
The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity
of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE®
sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition..
## Citation Information
```
@article{Tanabe2005,
author = {Lorraine Tanabe and Natalie Xie and Lynne H Thom and Wayne Matten and W John Wilbur},
title = {{GENETAG}: a tagged corpus for gene/protein named entity recognition},
journal = {{BMC} Bioinformatics},
volume = {6},
year = {2005},
url = {https://doi.org/10.1186/1471-2105-6-S1-S3},
doi = {10.1186/1471-2105-6-s1-s3},
biburl = {},
bibsource = {}
}
```
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