A phrase mining framework for recursive construction of a topical hierarchy

Chi Wang; Marina Danilevsky; Nihit Desai; Yinan Zhang; Phuong Nguyen; Thrivikrama Taula; Jiawei Han

doi:10.1145/2487575.2487631

A phrase mining framework for recursive construction of a topical hierarchy

Chi Wang, Marina Danilevsky, Nihit Desai, Yinan Zhang, Phuong Nguyen, Thrivikrama Taula, Jiawei Han

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

Abstract

A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.

Original language	English (US)
Title of host publication	KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editors	Rajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
Publisher	Association for Computing Machinery
Pages	437-445
Number of pages	9
ISBN (Electronic)	9781450321747
DOIs	https://doi.org/10.1145/2487575.2487631
State	Published - Aug 11 2013
Event	19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States Duration: Aug 11 2013 → Aug 14 2013

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume	Part F128815

Other

Other	19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country	United States
City	Chicago
Period	8/11/13 → 8/14/13

Keywords

Keyphrase extraction
Keyphrase ranking
Network analysis
Ontology learning
Topic modeling

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/2487575.2487631

Fingerprint Dive into the research topics of 'A phrase mining framework for recursive construction of a topical hierarchy'. Together they form a unique fingerprint.

Cite this

Wang, C., Danilevsky, M., Desai, N., Zhang, Y., Nguyen, P., Taula, T., & Han, J. (2013). A phrase mining framework for recursive construction of a topical hierarchy. In R. Parekh, J. He, D. S. Inderjit, P. Bradley, Y. Koren, R. Ghani, T. E. Senator, R. L. Grossman, & R. Uthurusamy (Eds.), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 437-445). [2487631] (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815). Association for Computing Machinery. https://doi.org/10.1145/2487575.2487631

A phrase mining framework for recursive construction of a topical hierarchy. / Wang, Chi; Danilevsky, Marina; Desai, Nihit; Zhang, Yinan; Nguyen, Phuong; Taula, Thrivikrama; Han, Jiawei.

KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ed. / Rajesh Parekh; Jingrui He; Dhillon S. Inderjit; Paul Bradley; Yehuda Koren; Rayid Ghani; Ted E. Senator; Robert L. Grossman; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. p. 437-445 2487631 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. Part F128815).

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

Wang, C, Danilevsky, M, Desai, N, Zhang, Y, Nguyen, P, Taula, T & Han, J 2013, A phrase mining framework for recursive construction of a topical hierarchy. in R Parekh, J He, DS Inderjit, P Bradley, Y Koren, R Ghani, TE Senator, RL Grossman & R Uthurusamy (eds), KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., 2487631, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. Part F128815, Association for Computing Machinery, pp. 437-445, 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, United States, 8/11/13. https://doi.org/10.1145/2487575.2487631

Wang C, Danilevsky M, Desai N, Zhang Y, Nguyen P, Taula T et al. A phrase mining framework for recursive construction of a topical hierarchy. In Parekh R, He J, Inderjit DS, Bradley P, Koren Y, Ghani R, Senator TE, Grossman RL, Uthurusamy R, editors, KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2013. p. 437-445. 2487631. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2487575.2487631

Wang, Chi ; Danilevsky, Marina ; Desai, Nihit ; Zhang, Yinan ; Nguyen, Phuong ; Taula, Thrivikrama ; Han, Jiawei. / A phrase mining framework for recursive construction of a topical hierarchy. KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. editor / Rajesh Parekh ; Jingrui He ; Dhillon S. Inderjit ; Paul Bradley ; Yehuda Koren ; Rayid Ghani ; Ted E. Senator ; Robert L. Grossman ; Ramasamy Uthurusamy. Association for Computing Machinery, 2013. pp. 437-445 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{42a9e2dddd20404b8213e4cccc86711d,

title = "A phrase mining framework for recursive construction of a topical hierarchy",

abstract = "A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.",

keywords = "Keyphrase extraction, Keyphrase ranking, Network analysis, Ontology learning, Topic modeling",

author = "Chi Wang and Marina Danilevsky and Nihit Desai and Yinan Zhang and Phuong Nguyen and Thrivikrama Taula and Jiawei Han",

note = "Funding Information: This work was supported in part by the U.S. National Science Foundation grants IIS–0905215, U.S. Army Research Laboratory under Cooperative Agreement No. W911NF– 09–2–0053 (NS-CTA). Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Research Fellowship grant NSF DGE 07-15088. The authors wish to acknowledge the University of Illinois at Urbana-Champaign Library (http://www.library.illinois. edu), which provided support for this research.; 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 ; Conference date: 11-08-2013 Through 14-08-2013",

year = "2013",

month = aug,

day = "11",

doi = "10.1145/2487575.2487631",

language = "English (US)",

series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

publisher = "Association for Computing Machinery",

pages = "437--445",

editor = "Rajesh Parekh and Jingrui He and Inderjit, {Dhillon S.} and Paul Bradley and Yehuda Koren and Rayid Ghani and Senator, {Ted E.} and Grossman, {Robert L.} and Ramasamy Uthurusamy",

booktitle = "KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",

}

TY - GEN

T1 - A phrase mining framework for recursive construction of a topical hierarchy

AU - Wang, Chi

AU - Danilevsky, Marina

AU - Desai, Nihit

AU - Zhang, Yinan

AU - Nguyen, Phuong

AU - Taula, Thrivikrama

AU - Han, Jiawei

N1 - Funding Information: This work was supported in part by the U.S. National Science Foundation grants IIS–0905215, U.S. Army Research Laboratory under Cooperative Agreement No. W911NF– 09–2–0053 (NS-CTA). Chi Wang was supported by a Microsoft Research PhD Fellowship. Marina Danilevsky was supported by a National Science Foundation Graduate Research Fellowship grant NSF DGE 07-15088. The authors wish to acknowledge the University of Illinois at Urbana-Champaign Library (http://www.library.illinois. edu), which provided support for this research.

PY - 2013/8/11

Y1 - 2013/8/11

N2 - A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.

AB - A high quality hierarchical organization of the concepts in a dataset at different levels of granularity has many valuable applications such as search, summarization, and content browsing. In this paper we propose an algorithm for recursively constructing a hierarchy of topics from a collection of content-representative documents. We characterize each topic in the hierarchy by an integrated ranked list of mixed-length phrases. Our mining framework is based on a phrase-centric view for clustering, extracting, and ranking topical phrases. Experiments with datasets from different domains illustrate our ability to generate hierarchies of high quality topics represented by meaningful phrases.

KW - Keyphrase extraction

KW - Keyphrase ranking

KW - Network analysis

KW - Ontology learning

KW - Topic modeling

UR - http://www.scopus.com/inward/record.url?scp=85016411521&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016411521&partnerID=8YFLogxK

U2 - 10.1145/2487575.2487631

DO - 10.1145/2487575.2487631

M3 - Conference contribution

AN - SCOPUS:85016411521

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 437

EP - 445

BT - KDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

A2 - Parekh, Rajesh

A2 - He, Jingrui

A2 - Inderjit, Dhillon S.

A2 - Bradley, Paul

A2 - Koren, Yehuda

A2 - Ghani, Rayid

A2 - Senator, Ted E.

A2 - Grossman, Robert L.

A2 - Uthurusamy, Ramasamy

PB - Association for Computing Machinery

T2 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013

Y2 - 11 August 2013 through 14 August 2013

ER -