research-article

A sentence level probabilistic model for evolutionary theme pattern mining from news corpora

Authors:

Nazli Goharian,

Ophir FriederAuthors Info & Claims

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

Pages 1742 - 1747

https://doi.org/10.1145/1529282.1529672

Published: 08 March 2009 Publication History

Abstract

Some recent topic model-based methods have been proposed to discover and summarize the evolutionary patterns of themes in temporal text collections. However, the theme patterns extracted by these methods are hard to interpret and evaluate. To produce a more descriptive representation of the theme pattern, we not only give new representations of sentences and themes with named entities, but we also propose a sentence-level probabilistic model based on the new representation pattern. Compared with other topic model methods, our approach not only gets each topic's distribution per term, but also generates candidate summary sentences of the themes as well. Consequently, the results are easier to understand and can be evaluated using the top sentences produced by our probabilistic model. Experimentation with the proposed methods on the Tsunami dataset shows that the proposed methods are useful in the discovery of evolutionary theme patterns.

References

[1]

J. Allan, R. Gupta, and V. Khandelwal. Temporal summaries of news topics. In Proceedings of ACM SIGIR 2001, pages 10--18, 2001.

Digital Library

[2]

D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.

Digital Library

[3]

T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pages 50--57, 1999.

Digital Library

[4]

T. M. Cover and J. A. Thomas. Elements of Information Theory. Wiley, 1991.

Digital Library

[5]

C. Zhai, A. Velivelli, and B. Yu. A cross-collection mixture model for comparative text mining. In Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pages 743--748, 2004.

Digital Library

[6]

D. M. Blei and J. D. Lafferty. Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning, 2006.

Digital Library

[7]

T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl. 1):5228--5235, 2004.

[8]

K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. Machine Learning, 2000.

Digital Library

[9]

Q. Mei and C. Zhai. Discovering evolutionary theme patterns from text - an exploration of temporal text mining. In Proceedings of the 11th International Conference on Knowledge Discovery and Data Mining (KDD'05), 2005.

Digital Library

[10]

X. Wang and A. McCallum. Topics over time: A non-markov continuous-time model of topical trends. In Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining (KDD'06), 2006.

Digital Library

[11]

S. Morinaga and K. Yamanishi. Tracking dynamics of topic trends using a finite mixture model. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04), 2004.

Digital Library

[12]

R. Kumar, U. Mahadevan, and D. Sivakumar. A graph-theoretic approach to extract storylines from search results. In Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pages 216--225, 2004.

Digital Library

[13]

Z. Li, B. W. anad Mingjing Li, and W. Y. Ma. A probabilistic model for retrospective news event detection. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'05), 2005.

Digital Library

[14]

Y. Yang, T. Pierce, and J. Carbonell. A study on retrospective and on-line event detection. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98), 1998.

Digital Library

[15]

R. Feldman and I. Dagan. Knowledge discovery in textual databases (kdt). In KDD, pages 112--117, 1995.

[16]

M. A. Hearst. Untangling text data mining. In Proceedings of the 37th conference on Association for Computational Linguistics (ACL 1999), pages 3--10, 1999.

Digital Library

[17]

J. Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 91--101, 2002.

Digital Library

[18]

A. Kontostathis, L. Galitsky, W. M. Pottenger, S. Roy, and D. J. Phelps. A survey of emerging trend detection in textual data mining. Survey of Text Mining, pages 185--224, 2003.

[19]

S. Morinaga and K. Yamanishi. Tracking dynamics of topic trends using a finite mixture model. In Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining, pages 811--816, 2004.

Digital Library

[20]

S. Roy, D. Gevry, and W. M. Pottenger. Methodologies for trend detection in textual data mining. In the Textmine '02 Workshop, Second SIAM International Conference on Data Mining, 2002.

[21]

Alias-I, "LingPipe," Website, 9 2006. {Online}. Available: http://www.alias-i.com/lingpipe/index.html

[22]

Topic detection and tracking (tdt) project. homepage: http://www.nist.gov/speech/tests/tdt/.

[23]

J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In Proc. of SIGIR Conference on Research and Development in Information Retrieval, 1998.

Digital Library

[24]

G. Kumaran and J. Allan. Text classification and named entities for new event detection. In Proc. of the SIGIR Conference on Research and Development in Information Retrieval, 2004.

Digital Library

[25]

G. Fung, J. Yu, H. Liu, P. Yu. Time-dependent event hierarchy construction. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07), 2007.

Digital Library

[26]

Nadeau D. and Sekine S. A Survey of Named Entity Recognition and Classification. In: Sekine, S. and Ranchhod, E. Named Entities: Recognition, classification and use. Special issue of Linguistics Investigationes. 30(1) pp. 3--26.

Cited By

Chung WMustaine EZeng D(2020)A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSSEnterprise Information Systems10.1080/17517575.2020.1827299(1-23)Online publication date: 27-Oct-2020
https://doi.org/10.1080/17517575.2020.1827299
Kong LYan RJiang HZhang YGao YFu L(2011)Mining event temporal boundaries from news corpora through evolution phase discoveryProceedings of the 12th international conference on Web-age information management10.5555/2035562.2035625(554-565)Online publication date: 14-Sep-2011
https://dl.acm.org/doi/10.5555/2035562.2035625
Kong LYan RHe YZhang YZhang ZFu L(2011)DVDProceedings of the 13th Asia-Pacific web conference on Web technologies and applications10.5555/1996794.1996817(168-180)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.5555/1996794.1996817
Show More Cited By

Index Terms

A sentence level probabilistic model for evolutionary theme pattern mining from news corpora
1. Information systems
  1. Information retrieval

Recommendations

Discovering evolutionary theme patterns from text: an exploration of temporal text mining
KDD '05: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining

Temporal Text Mining (TTM) is concerned with discovering temporal patterns in text information collected over time. Since most text information bears some time stamps, TTM has many applications in multiple domains, such as summarizing events in news ...
Theme chronicle model: chronicle consists of timestamp and topical words over each theme
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

This paper presents a topic model that discovers the correlation patterns in a given time-stamped document collection and how these patterns evolve over time. Our proposal, the theme chronicle model (TCM) divides traditional topics into temporal and ...
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model
WWW '16: Proceedings of the 25th International Conference on World Wide Web

Aspect-level sentiment analysis or opinion mining consists of several core sub-tasks: aspect extraction, opinion identification, polarity classification, and separation of general and aspect-specific opinions. Various topic models have been proposed by ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '09: Proceedings of the 2009 ACM symposium on Applied Computing

March 2009

2347 pages

ISBN:9781605581668

DOI:10.1145/1529282

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 March 2009

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SAC09

Sponsor:

SIGAPP

SAC09: The 2009 ACM Symposium on Applied Computing

March 8, 2009 - March 12, 2008

Hawaii, Honolulu

Acceptance Rates

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

Upcoming Conference

SAC '25

Sponsor:
sigapp

The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
289
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chung WMustaine EZeng D(2020)A computational framework for social-media-based business analytics and knowledge creation: empirical studies of CyTraSSEnterprise Information Systems10.1080/17517575.2020.1827299(1-23)Online publication date: 27-Oct-2020
https://doi.org/10.1080/17517575.2020.1827299
Kong LYan RJiang HZhang YGao YFu L(2011)Mining event temporal boundaries from news corpora through evolution phase discoveryProceedings of the 12th international conference on Web-age information management10.5555/2035562.2035625(554-565)Online publication date: 14-Sep-2011
https://dl.acm.org/doi/10.5555/2035562.2035625
Kong LYan RHe YZhang YZhang ZFu L(2011)DVDProceedings of the 13th Asia-Pacific web conference on Web technologies and applications10.5555/1996794.1996817(168-180)Online publication date: 18-Apr-2011
https://dl.acm.org/doi/10.5555/1996794.1996817
Zhang ZLi QZeng D(2011)Mining Evolutionary Topic Patterns in Community Question Answering SystemsIEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans10.1109/TSMCA.2011.215713141:5(828-833)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1109/TSMCA.2011.2157131
Kong LYan RJiang HZhang YGao YFu L(2011)Mining Event Temporal Boundaries from News Corpora through Evolution Phase DiscoveryWeb-Age Information Management10.1007/978-3-642-23535-1_47(554-565)Online publication date: 2011
https://doi.org/10.1007/978-3-642-23535-1_47
Kong LYan RHe YZhang YZhang ZFu L(2011)DVD: A Model for Event Diversified Versions DiscoveryWeb Technologies and Applications10.1007/978-3-642-20291-9_18(168-180)Online publication date: 2011
https://doi.org/10.1007/978-3-642-20291-9_18
Wu YDing YWang XXu J(2010)Topic detection by topic model induced distance using biased initiationProceedings of the 2010 international conference on Advances in computer science and information technology10.5555/1875558.1875588(310-323)Online publication date: 23-Jun-2010
https://dl.acm.org/doi/10.5555/1875558.1875588
Wu YDing YWang XXu J(2010)Topic Detection by Topic Model Induced Distance Using Biased InitiationAdvances in Computer Science and Information Technology10.1007/978-3-642-13577-4_27(310-323)Online publication date: 2010
https://doi.org/10.1007/978-3-642-13577-4_27

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten