| 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 = {} | |
| } | |
| ``` | |