TF-Miner: Topic-Specific Facet Mining by Label Propagation

Zhaotong Guo; Bifan Wei; Jun Liu; Bei Wu

doi:10.1007/978-3-030-18590-9_66

TF-Miner: Topic-Specific Facet Mining by Label Propagation

Zhaotong Guo
, Bifan Wei
, Jun Liu
, Bei Wu

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

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

3 Scopus citations

Abstract

Mining facets of topics is an essential task nowadays. Facet heterogeneity and long tail characteristic of information make facet mining tasks difficult. In this paper we propose a weakly supervised approach, called Topic-specific Facet (TF)-Miner, to mine TFs automatically by a Label Propagation algorithm (LPA). The process of propagation helps us mine complete facet sets. Experiments on several real-world datasets show that TF-Miner achieves better performance than the facet mining approaches which rely on the texts only.

Original language	English
Title of host publication	Database Systems for Advanced Applications - DASFAA 2019 International Workshops
Subtitle of host publication	BDMS, BDQM, and GDMA, Proceedings
Editors	Yongxin Tong, Jun Yang, Guoliang Li, Joao Gama, Juggapong Natwichai
Publisher	Springer Verlag
Pages	457-460
Number of pages	4
ISBN (Print)	9783030185893
DOIs	https://doi.org/10.1007/978-3-030-18590-9_66
State	Published - 2019
Event	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand Duration: 22 Apr 2019 → 25 Apr 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11448 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/Territory	Thailand
City	Chiang Mai
Period	22/04/19 → 25/04/19

Keywords

Contents section
Label Propagation
Topic similarity

Access to Document

10.1007/978-3-030-18590-9_66

Cite this

Guo, Z., Wei, B., Liu, J., & Wu, B. (2019). TF-Miner: Topic-Specific Facet Mining by Label Propagation. In Y. Tong, J. Yang, G. Li, J. Gama, & J. Natwichai (Eds.), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings (pp. 457-460). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_66

Guo, Zhaotong ; Wei, Bifan ; Liu, Jun et al. / TF-Miner : Topic-Specific Facet Mining by Label Propagation. Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. editor / Yongxin Tong ; Jun Yang ; Guoliang Li ; Joao Gama ; Juggapong Natwichai. Springer Verlag, 2019. pp. 457-460 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "TF-Miner: Topic-Specific Facet Mining by Label Propagation",

abstract = "Mining facets of topics is an essential task nowadays. Facet heterogeneity and long tail characteristic of information make facet mining tasks difficult. In this paper we propose a weakly supervised approach, called Topic-specific Facet (TF)-Miner, to mine TFs automatically by a Label Propagation algorithm (LPA). The process of propagation helps us mine complete facet sets. Experiments on several real-world datasets show that TF-Miner achieves better performance than the facet mining approaches which rely on the texts only.",

keywords = "Contents section, Label Propagation, Topic similarity",

author = "Zhaotong Guo and Bifan Wei and Jun Liu and Bei Wu",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 ; Conference date: 22-04-2019 Through 25-04-2019",

year = "2019",

doi = "10.1007/978-3-030-18590-9\_66",

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isbn = "9783030185893",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Guo, Z, Wei, B, Liu, J & Wu, B 2019, TF-Miner: Topic-Specific Facet Mining by Label Propagation. in Y Tong, J Yang, G Li, J Gama & J Natwichai (eds), Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11448 LNCS, Springer Verlag, pp. 457-460, 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, Chiang Mai, Thailand, 22/04/19. https://doi.org/10.1007/978-3-030-18590-9_66

TF-Miner: Topic-Specific Facet Mining by Label Propagation. / Guo, Zhaotong; Wei, Bifan; Liu, Jun et al.
Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. ed. / Yongxin Tong; Jun Yang; Guoliang Li; Joao Gama; Juggapong Natwichai. Springer Verlag, 2019. p. 457-460 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11448 LNCS).

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

TY - GEN

T1 - TF-Miner

T2 - 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019

AU - Guo, Zhaotong

AU - Wei, Bifan

AU - Liu, Jun

AU - Wu, Bei

PY - 2019

Y1 - 2019

N2 - Mining facets of topics is an essential task nowadays. Facet heterogeneity and long tail characteristic of information make facet mining tasks difficult. In this paper we propose a weakly supervised approach, called Topic-specific Facet (TF)-Miner, to mine TFs automatically by a Label Propagation algorithm (LPA). The process of propagation helps us mine complete facet sets. Experiments on several real-world datasets show that TF-Miner achieves better performance than the facet mining approaches which rely on the texts only.

AB - Mining facets of topics is an essential task nowadays. Facet heterogeneity and long tail characteristic of information make facet mining tasks difficult. In this paper we propose a weakly supervised approach, called Topic-specific Facet (TF)-Miner, to mine TFs automatically by a Label Propagation algorithm (LPA). The process of propagation helps us mine complete facet sets. Experiments on several real-world datasets show that TF-Miner achieves better performance than the facet mining approaches which rely on the texts only.

KW - Contents section

KW - Label Propagation

KW - Topic similarity

UR - https://www.scopus.com/pages/publications/85065408361

U2 - 10.1007/978-3-030-18590-9_66

DO - 10.1007/978-3-030-18590-9_66

M3 - 会议稿件

AN - SCOPUS:85065408361

SN - 9783030185893

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 457

EP - 460

BT - Database Systems for Advanced Applications - DASFAA 2019 International Workshops

A2 - Tong, Yongxin

A2 - Yang, Jun

A2 - Li, Guoliang

A2 - Gama, Joao

A2 - Natwichai, Juggapong

PB - Springer Verlag

Y2 - 22 April 2019 through 25 April 2019

ER -

Guo Z, Wei B, Liu J, Wu B. TF-Miner: Topic-Specific Facet Mining by Label Propagation. In Tong Y, Yang J, Li G, Gama J, Natwichai J, editors, Database Systems for Advanced Applications - DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Proceedings. Springer Verlag. 2019. p. 457-460. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-18590-9_66

TF-Miner: Topic-Specific Facet Mining by Label Propagation

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