Jointly modeling and simultaneously discovering topics and clusters in text corpora using word vectors

DOI10.1016/j.ins.2021.01.019OpenAlexW3122481580MaRDI QIDQ6086262

Gianni Costa, Riccardo Ortale

Publication date: 9 November 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.01.019

zbMATH Keywords

topic modelingdocument clusteringword embeddingsBayesian text analysis

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items

Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters ⋮ Robust subspace clustering based on automatic weighted multiple kernel learning

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A tutorial on Bayesian nonparametric models
• An introduction to MCMC for machine learning
• Text Mining
• 10.1162/153244303322533223
• 10.1162/jmlr.2003.3.4-5.993
• Partitional Clustering Algorithms