Cargando…
OryzaGP 2021 update: a rice gene and protein dataset for named-entity recognition
Due to the rapid evolution of high-throughput technologies, a tremendous amount of data is being produced in the biological domain, which poses a challenging task for information extraction and natural language understanding. Biological named entity recognition (NER) and named entity normalisation (...
Autores principales: | Larmande, Pierre, Liu, Yusha, Yao, Xinzhi, Xia, Jingbo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korea Genome Organization
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510865/ https://www.ncbi.nlm.nih.gov/pubmed/34638174 http://dx.doi.org/10.5808/gi.21015 |
Ejemplares similares
-
OryzaGP: rice gene and protein dataset for named-entity recognition
por: Larmande, Pierre, et al.
Publicado: (2019) -
Improving classification of low-resource COVID-19 literature by using Named Entity Recognition
por: Lithgow-Serrano, Oscar, et al.
Publicado: (2021) -
A biomedically oriented automatically annotated Twitter COVID-19 dataset
por: Hernandez, Luis Alberto Robles, et al.
Publicado: (2021) -
LitCovid-AGAC: cellular and molecular level annotation data set based on COVID-19
por: Ouyang, Sizhuo, et al.
Publicado: (2021) -
O-JMeSH: creating a bilingual English-Japanese controlled vocabulary of MeSH UIDs through machine translation and mutual information
por: Soares, Felipe, et al.
Publicado: (2021)