Cargando…
A semi-supervised approach for extracting TCM clinical terms based on feature words
BACKGROUND: A semi-supervised model is proposed for extracting clinical terms of Traditional Chinese Medicine using feature words. METHODS: The extraction model is based on BiLSTM-CRF and combined with semi-supervised learning and feature word set, which reduces the cost of manual annotation and lev...
Autores principales: | Liu, Liangliang, Wu, Xiaojing, Liu, Hui, Cao, Xinyu, Wang, Haitao, Zhou, Hongwei, Xie, Qi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7477860/ https://www.ncbi.nlm.nih.gov/pubmed/32646408 http://dx.doi.org/10.1186/s12911-020-1108-1 |
Ejemplares similares
-
Generative Adversarial Training for Supervised and Semi-supervised Learning
por: Wang, Xianmin, et al.
Publicado: (2022) -
Semi-Supervised Fuzzy Clustering with Feature Discrimination
por: Li, Longlong, et al.
Publicado: (2015) -
Simple strategies for semi-supervised feature selection
por: Sechidis, Konstantinos, et al.
Publicado: (2017) -
Semi-supervised method for biomedical event extraction
por: Wang, Jian, et al.
Publicado: (2013) -
Semi-Supervised Feature Transformation for Tissue Image Classification
por: Watanabe, Kenji, et al.
Publicado: (2016)