Discovering Dynamic Topic Transitions in Topic Models

Olga Isupova; Danil Kuzin; Lyudmila Mihaylova

Discovering Dynamic Topic Transitions in Topic Models

Olga Isupova, Danil Kuzin, Lyudmila Mihaylova

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.

Original language	English
Publication status	Published - 2018
Event	All of Bayesian Nonparametrics (Especially the Useful Bits): A workshop at the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018). - Palais des Congrès de Montréal, Montréal, Canada Duration: 7 Dec 2018 → 7 Dec 2018 https://sites.google.com/view/nipsbnp2018/home

Workshop

Workshop	All of Bayesian Nonparametrics (Especially the Useful Bits)
Abbreviated title	BNP@NeurIPS 2018
Country/Territory	Canada
City	Montréal
Period	7/12/18 → 7/12/18
Internet address	https://sites.google.com/view/nipsbnp2018/home

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@conference{3fff61163cd042b5a5387e2eab499d3b,

title = "Discovering Dynamic Topic Transitions in Topic Models",

abstract = "In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.",

author = "Olga Isupova and Danil Kuzin and Lyudmila Mihaylova",

year = "2018",

language = "English",

note = "All of Bayesian Nonparametrics (Especially the Useful Bits) : A workshop at the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018)., BNP@NeurIPS 2018 ; Conference date: 07-12-2018 Through 07-12-2018",

url = "https://sites.google.com/view/nipsbnp2018/home",

}

TY - CONF

T1 - Discovering Dynamic Topic Transitions in Topic Models

AU - Isupova, Olga

AU - Kuzin, Danil

AU - Mihaylova, Lyudmila

PY - 2018

Y1 - 2018

N2 - In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.

AB - In this paper we propose a Dirichlet process topic model for representing patterns in complex dynamic data. We assume that in a dynamic corpus topics do not randomly appear and disappear but rather replace each other following transitional rules. We model topic transitions as a Markov chain with an infinite hidden Markov model. The proposed model is then formulated as a Dirichlet process with a hierarchical Dirichlet process hidden Markov model prior.

M3 - Paper

T2 - All of Bayesian Nonparametrics (Especially the Useful Bits)

Y2 - 7 December 2018 through 7 December 2018

ER -

Discovering Dynamic Topic Transitions in Topic Models

Abstract

Workshop

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