poster

Dynamic hyperparameter optimization for bayesian topical trend analysis

Authors:

Tomonari Masada,

Daiji Fukagawa,

Atsuhiro Takasu,

Tsuyoshi Hamada,

Yuichiro Shibata,

Kiyoshi OguriAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1831 - 1834

https://doi.org/10.1145/1645953.1646242

Published: 02 November 2009 Publication History

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.

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Cited By

Han JHuang YKumar KBhattacharya S(2018)Time-Varying Dynamic Topic ModelJournal of Global Information Management10.4018/JGIM.201801010626:1(104-119)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/JGIM.2018010106
Yang ZMa HHe ZWang X(2018)Finding maximal ranges with unique topics in a text databaseWorld Wide Web10.1007/s11280-017-0448-y21:2(289-310)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11280-017-0448-y
Wistuba MSchilling NSchmidt-Thieme L(2018)Scalable Gaussian process-based transfer surrogates for hyperparameter optimizationMachine Language10.1007/s10994-017-5684-y107:1(43-78)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10994-017-5684-y
Show More Cited By

Index Terms

Dynamic hyperparameter optimization for bayesian topical trend analysis
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 November 2009

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CIKM '09: Conference on Information and Knowledge Management

November 2 - 6, 2009

Hong Kong, China

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Han JHuang YKumar KBhattacharya S(2018)Time-Varying Dynamic Topic ModelJournal of Global Information Management10.4018/JGIM.201801010626:1(104-119)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/JGIM.2018010106
Yang ZMa HHe ZWang X(2018)Finding maximal ranges with unique topics in a text databaseWorld Wide Web10.1007/s11280-017-0448-y21:2(289-310)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11280-017-0448-y
Wistuba MSchilling NSchmidt-Thieme L(2018)Scalable Gaussian process-based transfer surrogates for hyperparameter optimizationMachine Language10.1007/s10994-017-5684-y107:1(43-78)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s10994-017-5684-y
Chen LZhang HJose JYu HMoshfeghi YTriantafillou P(2018)Topic detection and tracking on heterogeneous informationJournal of Intelligent Information Systems10.1007/s10844-017-0487-y51:1(115-137)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s10844-017-0487-y
Diao QJiang JZhu FLim ELi HLin COsborne M(2012)Finding bursty topics from microblogsProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 110.5555/2390524.2390599(536-544)Online publication date: 8-Jul-2012
https://dl.acm.org/doi/10.5555/2390524.2390599
Hong LDom BGurumurthy STsioutsiouliklis KApte CGhosh JSmyth P(2011)A time-dependent topic model for multiple text streamsProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2020408.2020551(832-840)Online publication date: 21-Aug-2011
https://dl.acm.org/doi/10.1145/2020408.2020551
Hong LYin DGuo JDavison BApte CGhosh JSmyth P(2011)Tracking trendsProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2020408.2020485(484-492)Online publication date: 21-Aug-2011
https://dl.acm.org/doi/10.1145/2020408.2020485
Masada TFukagawa DTakasu AShibata YOguri K(2010)Modeling topical trends over continuous time with priorsProceedings of the 7th international conference on Advances in Neural Networks - Volume Part II10.1007/978-3-642-13318-3_38(302-311)Online publication date: 6-Jun-2010
https://dl.acm.org/doi/10.1007/978-3-642-13318-3_38

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