Cargando…
Relation Extraction in Biomedical Texts Based on Multi-Head Attention Model With Syntactic Dependency Feature: Modeling Study
BACKGROUND: With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic. OBJECTIVE: This study aimed to perform 2 multiclass relation extr...
Autores principales: | Li, Yongbin, Hui, Linhu, Zou, Liping, Li, Huyang, Xu, Luo, Wang, Xiaohua, Chua, Stephanie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634522/ https://www.ncbi.nlm.nih.gov/pubmed/36264604 http://dx.doi.org/10.2196/41136 |
Ejemplares similares
-
Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations
por: Li, Yongbin, et al.
Publicado: (2020) -
Improving biomedical named entity recognition with syntactic information
por: Tian, Yuanhe, et al.
Publicado: (2020) -
Attention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network
por: Jia, Xudong, et al.
Publicado: (2022) -
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) -
Temporal Relation Extraction with Joint Semantic and Syntactic Attention
por: Jin, Panpan, et al.
Publicado: (2022)