Unsupervised Word Embedding Learning by Incorporating Local and Global Contexts

Word embedding has benefited a broad spectrum of text analysis tasks by learning distributed word representations to encode word semantics. Word representations are typically learned by modeling local contexts of words, assuming that words sharing similar surrounding words are semantically close. We...

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Detalles Bibliográficos
Autores principales: Meng, Yu, Huang, Jiaxin, Wang, Guangyuan, Wang, Zihan, Zhang, Chao, Han, Jiawei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931948/
https://www.ncbi.nlm.nih.gov/pubmed/33693384
http://dx.doi.org/10.3389/fdata.2020.00009

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931948/
https://www.ncbi.nlm.nih.gov/pubmed/33693384
http://dx.doi.org/10.3389/fdata.2020.00009

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