Hierarchical Bayesian text modeling for the unsupervised joint analysis of latent topics and semantic clusters

DOI10.1016/J.IJAR.2022.05.002OpenAlexW4280503546MaRDI QIDQ2152511

Riccardo Ortale, Gianni Costa

Publication date: 8 July 2022

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2022.05.002

zbMATH Keywords

hierarchical priorstopic modelingdocument clusteringBayesian text analysis

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

New metrics and tests for subject prevalence in documents based on topic modeling

Uses Software

• BayesDA
• PMTK
• word2vec
• Tensor2Tensor
• BERT
• UMAP
• PRMLT

Cites Work

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• PRMLT
• An introduction to MCMC for machine learning
• An introduction to variational methods for graphical models
• Text Mining
• 10.1162/153244303322533223
• 10.1162/jmlr.2003.3.4-5.993
• Partitional Clustering Algorithms
• Jointly modeling and simultaneously discovering topics and clusters in text corpora using word vectors

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