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Hierarchical bayesian modeling of topics in time-stamped documents.

Publication ,  Journal Article
Pruteanu-Malinici, I; Ren, L; Paisley, J; Wang, E; Carin, L
Published in: IEEE transactions on pattern analysis and machine intelligence
June 2010

We consider the problem of inferring and modeling topics in a sequence of documents with known publication dates. The documents at a given time are each characterized by a topic and the topics are drawn from a mixture model. The proposed model infers the change in the topic mixture weights as a function of time. The details of this general framework may take different forms, depending on the specifics of the model. For the examples considered here, we examine base measures based on independent multinomial-Dirichlet measures for representation of topic-dependent word counts. The form of the hierarchical model allows efficient variational Bayesian inference, of interest for large-scale problems. We demonstrate results and make comparisons to the model when the dynamic character is removed, and also compare to latent Dirichlet allocation (LDA) and Topics over Time (TOT). We consider a database of Neural Information Processing Systems papers as well as the US Presidential State of the Union addresses from 1790 to 2008.

Duke Scholars

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2010

Volume

32

Issue

6

Start / End Page

996 / 1011

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Pruteanu-Malinici, I., Ren, L., Paisley, J., Wang, E., & Carin, L. (2010). Hierarchical bayesian modeling of topics in time-stamped documents. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), 996–1011. https://doi.org/10.1109/tpami.2009.125
Pruteanu-Malinici, Iulian, Lu Ren, John Paisley, Eric Wang, and Lawrence Carin. “Hierarchical bayesian modeling of topics in time-stamped documents.IEEE Transactions on Pattern Analysis and Machine Intelligence 32, no. 6 (June 2010): 996–1011. https://doi.org/10.1109/tpami.2009.125.
Pruteanu-Malinici I, Ren L, Paisley J, Wang E, Carin L. Hierarchical bayesian modeling of topics in time-stamped documents. IEEE transactions on pattern analysis and machine intelligence. 2010 Jun;32(6):996–1011.
Pruteanu-Malinici, Iulian, et al. “Hierarchical bayesian modeling of topics in time-stamped documents.IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, June 2010, pp. 996–1011. Epmc, doi:10.1109/tpami.2009.125.
Pruteanu-Malinici I, Ren L, Paisley J, Wang E, Carin L. Hierarchical bayesian modeling of topics in time-stamped documents. IEEE transactions on pattern analysis and machine intelligence. 2010 Jun;32(6):996–1011.

Published In

IEEE transactions on pattern analysis and machine intelligence

DOI

EISSN

1939-3539

ISSN

0162-8828

Publication Date

June 2010

Volume

32

Issue

6

Start / End Page

996 / 1011

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 0906 Electrical and Electronic Engineering
  • 0806 Information Systems
  • 0801 Artificial Intelligence and Image Processing