Cargando…
Improving biomedical named entity recognition with syntactic information
BACKGROUND: Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and Bio...
Autores principales: | Tian, Yuanhe, Shen, Wang, Song, Yan, Xia, Fei, He, Min, Li, Kenli |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687711/ https://www.ncbi.nlm.nih.gov/pubmed/33238875 http://dx.doi.org/10.1186/s12859-020-03834-6 |
Ejemplares similares
-
Improving deep learning method for biomedical named entity recognition by using entity definition information
por: Xiong, Ying, et al.
Publicado: (2021) -
Named Entity Recognition and Relation Detection for Biomedical Information Extraction
por: Perera, Nadeesha, et al.
Publicado: (2020) -
BioByGANS: biomedical named entity recognition by fusing contextual and syntactic features through graph attention network in node classification framework
por: Zheng, Xiangwen, et al.
Publicado: (2022) -
Comparison of named entity recognition methodologies in biomedical documents
por: Song, Hye-Jeong, et al.
Publicado: (2018) -
Various criteria in the evaluation of biomedical named entity recognition
por: Tsai, Richard Tzong-Han, et al.
Publicado: (2006)