Fine-Tuning Word Embeddings for Hierarchical Representation of Data Using a Corpus and a Knowledge Base for Various Machine Learning Applications
Word embedding models have recently shown some capability to encode hierarchical information that exists in textual data. However, such models do not explicitly encode the hierarchical structure that exists among words. In this work, we propose a method to learn hierarchical word embeddings (HWEs) i...
Guardado en:
Autores principales: | Mohammed Alsuhaibani, Danushka Bollegala |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Hindawi Limited
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b586f8dd6686407b98ccb44fe586a29b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Natural language word embeddings as a glimpse into healthcare language and associated mortality surrounding end of life
por: Wei Gao, et al.
Publicado: (2021) -
A hierarchical deep learning approach with transparency and interpretability based on small samples for glaucoma diagnosis
por: Yongli Xu, et al.
Publicado: (2021) -
Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
por: Laila Rasmy, et al.
Publicado: (2021) -
Deep representation learning of electronic health records to unlock patient stratification at scale
por: Isotta Landi, et al.
Publicado: (2020) -
A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication
por: Negar Farzaneh, et al.
Publicado: (2021)