Hierarchical topic integration through semi-supervised hierarchical topic modeling

Xian Ling Mao; Jing He; Hongfei Yan; Xiaoming Li

doi:10.1145/2396761.2398483

Hierarchical topic integration through semi-supervised hierarchical topic modeling

Xian Ling Mao, Jing He, Hongfei Yan^*, Xiaoming Li

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

Abstract

Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.

Original language	English
Title of host publication	CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages	1612-1616
Number of pages	5
DOIs	https://doi.org/10.1145/2396761.2398483
Publication status	Published - 2012
Externally published	Yes
Event	21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: 29 Oct 2012 → 2 Nov 2012

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/Territory	United States
City	Maui, HI
Period	29/10/12 → 2/11/12

Keywords

hierarchical topic modeling
topical integration

Access to Document

10.1145/2396761.2398483

Cite this

@inproceedings{18a8202685fe43dd9489e09fb4b3448b,

title = "Hierarchical topic integration through semi-supervised hierarchical topic modeling",

abstract = "Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.",

keywords = "hierarchical topic modeling, topical integration",

author = "Mao, {Xian Ling} and Jing He and Hongfei Yan and Xiaoming Li",

year = "2012",

doi = "10.1145/2396761.2398483",

language = "English",

isbn = "9781450311564",

series = "ACM International Conference Proceeding Series",

pages = "1612--1616",

booktitle = "CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management",

note = "21st ACM International Conference on Information and Knowledge Management, CIKM 2012 ; Conference date: 29-10-2012 Through 02-11-2012",

}

Mao, XL, He, J, Yan, H & Li, X 2012, Hierarchical topic integration through semi-supervised hierarchical topic modeling. in CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM International Conference Proceeding Series, pp. 1612-1616, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 29/10/12. https://doi.org/10.1145/2396761.2398483

Hierarchical topic integration through semi-supervised hierarchical topic modeling. / Mao, Xian Ling; He, Jing; Yan, Hongfei et al.
CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 1612-1616 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hierarchical topic integration through semi-supervised hierarchical topic modeling

AU - Mao, Xian Ling

AU - He, Jing

AU - Yan, Hongfei

AU - Li, Xiaoming

PY - 2012

Y1 - 2012

N2 - Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.

AB - Lots of document collections are well organized in hierarchical structure, and such structure can help users browse and understand these collections. Meanwhile, there are a large number of plain document collections loosely organized, and it is difficult for users to understand them effectively. In this paper we study how to automatically integrate latent topics in a plain collection with the topics in a hierarchical structured collection. We propose to use semi-supervised topic modeling to solve the problem in a principled way. The experiments show that the proposed method can generate both meaningful latent topics and expand high quality hierarchical topic structures.

KW - hierarchical topic modeling

KW - topical integration

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U2 - 10.1145/2396761.2398483

DO - 10.1145/2396761.2398483

M3 - Conference contribution

AN - SCOPUS:84871047720

SN - 9781450311564

T3 - ACM International Conference Proceeding Series

SP - 1612

EP - 1616

BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management

T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012

Y2 - 29 October 2012 through 2 November 2012

ER -

Hierarchical topic integration through semi-supervised hierarchical topic modeling

Abstract

Publication series

Conference

Keywords

Access to Document

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