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Dynamic hyperparameter optimization for bayesian topical trend analysis

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Title: Dynamic hyperparameter optimization for bayesian topical trend analysis
Authors: Masada, Tomonari / Fukagawa, Daiji / Takasu, Atsuhiro / Hamada, Tsuyoshi / Shibata, Yuichiro / Oguri, Kiyoshi
Issue Date: 2009
Publisher: ACM Press
Citation: Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09, pp1831-1834; 2009
Abstract: This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute TFIDF-based document similarities by using a result of collapsed Gibbs sampling and evaluate our proposal by link detection task of Topic Detection and Tracking.
Description: Proceeding of the 18th ACM conference : Hong Kong, China, 2009.11.02-2009.11.06
Keywords: Temporal analysis / Topic detection / Topic modeling
URI: http://hdl.handle.net/10069/22901
ISBN: 978-160558512-3
DOI: 10.1145/1645953.1646242
Rights: © ACM 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09. http://doi.acm.org/10.1145/1645953.1646242
Type: Conference Paper
Text Version: author
Appears in Collections:Conference Paper

Citable URI : http://hdl.handle.net/10069/22901

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