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A network approach to topic models

Published:2018-07-06 Issue:7 Volume:4 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Gerlach Martin¹²^ORCID,Peixoto Tiago P.³⁴^ORCID,Altmann Eduardo G.²⁵^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL 60208, USA.

2. Max Planck Institute for the Physics of Complex Systems, D-01187 Dresden, Germany.

3. Department of Mathematical Sciences and Centre for Networks and Collective Behaviour, University of Bath, Claverton Down, Bath BA2 7AY, UK.

4. Institute for Scientific Interchange Foundation, Via Alassio 11/c, 10126 Torino, Italy.

5. School of Mathematics and Statistics, University of Sydney, 2006 New South Wales, Australia.

Abstract

A new approach to topic models finds topics through community detection in word-document networks.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference61 articles.

1. Probabilistic topic models;Blei D. M.;Commun. ACM,2012

2. Indexing by latent semantic analysis;Deerwester S.;J. Am. Soc. Inf. Sci.,1990

3. Probabilistic machine learning and artificial intelligence

4. T. Hofmann Probabilistic latent semantic indexing in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99) Berkeley CA 15 to 19 August 1999 pp. 50–57.

5. Latent Dirichlet allocation;Blei D. M.;J. Mach. Learn. Res.,2003

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