A supervised topic embedding model and its application

We propose rTopicVec, a supervised topic embedding model that predicts response variables associated with documents by analyzing the text data. Topic modeling leverages document-level word co-occurrence patterns to learn latent topics of each document. While word embedding is a promising text analys...

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Detalles Bibliográficos
Autores principales: Xu, Weiran, Eguchi, Koji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635756/
https://www.ncbi.nlm.nih.gov/pubmed/36331905
http://dx.doi.org/10.1371/journal.pone.0277104

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635756/
https://www.ncbi.nlm.nih.gov/pubmed/36331905
http://dx.doi.org/10.1371/journal.pone.0277104

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