Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Lihui LIN; Yanghui RAO; Haoran XIE; Raymond Y. K. LAU; Jian YIN; Fu Lee WANG; Qing LI

doi:10.1109/ICDE55515.2023.00338

Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Lihui LIN
, Yanghui RAO
, Haoran XIE
, Raymond Y. K. LAU
, Jian YIN
, Fu Lee WANG
, Qing LI

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference (Extended Abstracts) › peer-review

Abstract

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

Original language	English
Title of host publication	Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
Pages	3823-3824
Number of pages	2
ISBN (Electronic)	9798350322279
DOIs	https://doi.org/10.1109/ICDE55515.2023.00338
Publication status	Published - 26 Jul 2023
Event	2023 IEEE 39th International Conference on Data Engineering (ICDE) - Anaheim, United States Duration: 3 Apr 2023 → 7 Apr 2023 https://icde2023.ics.uci.edu/

Publication series

Name	Proceedings - International Conference on Data Engineering
Volume	2023-April
ISSN (Print)	1084-4627

Conference

Conference	2023 IEEE 39th International Conference on Data Engineering (ICDE)
Country/Territory	United States
City	Anaheim
Period	3/04/23 → 7/04/23
Internet address	https://icde2023.ics.uci.edu/

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Funding

This work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19), the National Natural Science Foundation of China (61972426), the Direct Grant (DR23B2) and the Faculty Research Grant (DB23A3) of Lingnan University, Hong Kong, a grant from the Research Grants Council of the HKSAR, China (Project: CityU 11507219), and a grant from the City University of Hong Kong SRG (Project: 7005780).

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

copulas
parallel gibbs sampling
topic modelling

Access to Document

10.1109/ICDE55515.2023.00338

https://ieeexplore.ieee.org/document/10184894/

Cite this

LIN, L., RAO, Y., XIE, H., LAU, R. Y. K., YIN, J., WANG, F. L., & LI, Q. (2023). Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract). In Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023 (pp. 3823-3824). (Proceedings - International Conference on Data Engineering; Vol. 2023-April). https://doi.org/10.1109/ICDE55515.2023.00338

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title = "Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)",

abstract = "In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.",

keywords = "copulas, parallel gibbs sampling, topic modelling",

author = "Lihui LIN and Yanghui RAO and Haoran XIE and LAU, \{Raymond Y. K.\} and Jian YIN and WANG, \{Fu Lee\} and Qing LI",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE 39th International Conference on Data Engineering (ICDE) ; Conference date: 03-04-2023 Through 07-04-2023",

year = "2023",

month = jul,

day = "26",

doi = "10.1109/ICDE55515.2023.00338",

language = "English",

isbn = "9798350322279",

series = "Proceedings - International Conference on Data Engineering",

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booktitle = "Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023",

url = "https://icde2023.ics.uci.edu/",

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LIN, L, RAO, Y, XIE, H, LAU, RYK, YIN, J, WANG, FL & LI, Q 2023, Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract). in Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. Proceedings - International Conference on Data Engineering, vol. 2023-April, pp. 3823-3824, 2023 IEEE 39th International Conference on Data Engineering (ICDE), Anaheim, California, United States, 3/04/23. https://doi.org/10.1109/ICDE55515.2023.00338

Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract). / LIN, Lihui; RAO, Yanghui; XIE, Haoran et al.
Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023. 2023. p. 3823-3824 (Proceedings - International Conference on Data Engineering; Vol. 2023-April).

Research output: Book Chapters | Papers in Conference Proceedings › Conference (Extended Abstracts) › peer-review

TY - CHAP

T1 - Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

AU - LIN, Lihui

AU - RAO, Yanghui

AU - XIE, Haoran

AU - LAU, Raymond Y. K.

AU - YIN, Jian

AU - WANG, Fu Lee

AU - LI, Qing

PY - 2023/7/26

Y1 - 2023/7/26

N2 - In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

AB - In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

KW - copulas

KW - parallel gibbs sampling

KW - topic modelling

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DO - 10.1109/ICDE55515.2023.00338

M3 - Conference (Extended Abstracts)

SN - 9798350322279

T3 - Proceedings - International Conference on Data Engineering

SP - 3823

EP - 3824

BT - Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023

T2 - 2023 IEEE 39th International Conference on Data Engineering (ICDE)

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ER -

Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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Cite this