Cargando…
A hybrid deep learning framework for bacterial named entity recognition with domain features
BACKGROUND: Microbes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions w...
Autores principales: | Li, Xusheng, Fu, Chengcheng, Zhong, Ran, Zhong, Duo, He, Tingting, Jiang, Xingpeng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6886245/ https://www.ncbi.nlm.nih.gov/pubmed/31787075 http://dx.doi.org/10.1186/s12859-019-3071-3 |
Ejemplares similares
-
Recognition of bacteria named entity using conditional random fields in Spark
por: Wang, Xiaoyan, et al.
Publicado: (2018) -
Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning
por: Zhang, Yaoyun, et al.
Publicado: (2016) -
Towards reliable named entity recognition in the biomedical domain
por: Giorgi, John M, et al.
Publicado: (2020) -
Using BERT and Augmentation in Named Entity Recognition for Cybersecurity Domain
por: Tikhomirov, Mikhail, et al.
Publicado: (2020) -
Named Entity Recognition for Bacterial Type IV Secretion Systems
por: Ananiadou, Sophia, et al.
Publicado: (2011)