Article

Free access

Extracting significant time varying features from text

Authors:

Russell Swan,

James AllanAuthors Info & Claims

CIKM '99: Proceedings of the eighth international conference on Information and knowledge management

Pages 38 - 45

https://doi.org/10.1145/319950.319956

Published: 01 November 1999 Publication History

PDF eReader

Abstract

We propose a simple statistical model for the frequency of occurrence of features in a stream of text. Adoption of this model allows us to use classical significance tests to filter the stream for interesting events. We tested the model by building a system and running it on a news corpus. By a subjective evaluation, the system worked remarkably well: almost all of the groups of identified tokens corresponded to news stories and were appropriately placed in time. A preliminary objective evaluation was also used to measure the quality of the system and it showed some of the weaknesses and the power of our approach.

References

[1]

Facts on File, 1996. Facts on File, New York, 1997.

Google Scholar

[2]

J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang. Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA Broadcast News 2kanscription and Understanding Workshop, pages 194-218, 1998.

Google Scholar

[3]

R. B. Allen. Timelines as information system interfaces. In Proceedings International Symposium on Digital Libraries, pages 175-180, Tsukuba, Japan, 1995.

Google Scholar

[4]

Yvonne M. M. Bishop, Stephen E. Feinberg, and Paul W. Holland. Discrete multivariate analysis: theory and practice. MIT Press, Cambridge, Massachusetts, 1974.

Google Scholar

[5]

Ido Dagan and Ronen Feldman. Keyword-based browsing and analysis of large document sets. In Proceedings o/the Symposium on Document Analysis and Information Retrieval (SDAiR-96), Las Vegas, Nevada, 1996.

Google Scholar

[6]

D. Fisher, S. Soderland, J. McCarthy, F. Feng, and W. Lehnert. Description of the umass systems as used for muc-6. In Proceedings of the 6th Message Understanding Conjference, November, I995, pages 127-140, 1996.

Digital Library

Google Scholar

[7]

Robin L. Kultberg. Dynamic timelines: Visualizing historical information in three dimensions. Master's thesis, Massachusetts Institute of Technology Media Laboratory, 1995.

Google Scholar

[8]

E. L. Margulis. Modeling documents with multiple poisson distributions, information Processing and Management, 29(2):215-227, 1993.

Digital Library

Google Scholar

[9]

Ron Papka, James Allan, and Victor Lavrenko. Umass approaches to detection and tracking at TDT2. In Proceedings of the DARPA Broadcast Workshop, 1999.

Google Scholar

[10]

C. PlaJsant, B. Milash, A. Rose, S. Widoff, and B. Shneiderman. Life lines: Visualizing personal histories. In CHI'96 Conference Proceedings, pages 221-227, Vancouver, BC, Canada, 1996.

Digital Library

Google Scholar

[11]

M. Sanderson and W. B. Croft. Deriving structure from texts. In Proceedings o/the 22nd International A CM SIGIR Conference on Research and Development in Information Retrieval (SIGIR99), 1999.

Digital Library

Google Scholar

[12]

Jinxi Xu, J. Broglio, and W. B. Croft. The design and implementation of a part of speech tagger for english. Technical Report IR-52, Center for Intelligent Information Retrieval, University of Massachusetts, Amherst, 1994.

Digital Library

Google Scholar

Cited By

View all

Campos CKappelhof RCarrilho JMidão LHenriques DCosta E(2024)Role of twinnings in scaling up innovative solutions across EuropeBMJ Innovations10.1136/bmjinnov-2023-001141(bmjinnov-2023-001141)Online publication date: 15-Mar-2024
https://doi.org/10.1136/bmjinnov-2023-001141
Lee GSun A(2021)Understanding the stability of medical concept embeddingsJournal of the Association for Information Science and Technology10.1002/asi.2441172:3(346-356)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24411
Schultheiß SLewandowski D(2021)How users' knowledge of advertisements influences their viewing and selection behavior in search enginesJournal of the Association for Information Science and Technology10.1002/asi.2441072:3(285-301)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24410
Show More Cited By

Index Terms

Extracting significant time varying features from text

Recommendations

Significant motifs in time series

Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of ...
Extracting lexical knowledge from dictionary text
Special issue on knowledge acquisition

A lexicon providing subsystems, that produces text-specific lexicons from selected machine readable dictionary definitions from the Longman Dictionary of Contemporary English (LDOCE; Procter, 1978) has been developed. Although designed as part of a ...
Sentence Features Fusion for Text Summarization Using Fuzzy Logic
HIS '09: Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01

The scoring mechanism of the text features is the unique way for determining the key ideas in the text to be presented as text summary. The efficiency of the technique used for scoring the text sentences could produce good summary. The feature scores ...

Comments

Information & Contributors

Information

Published In

CIKM '99: Proceedings of the eighth international conference on Information and knowledge management

November 1999

564 pages

ISBN:1581131461

DOI:10.1145/319950

Editor:
Susan Gauch

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 1999

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

CIKM99

Sponsor:

CIKM99: Conference on Information and Knowledge Management

November 2 - 6, 1999

Missouri, Kansas City, USA

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

70
Total Citations
View Citations
1,193
Total Downloads

Downloads (Last 12 months)283
Downloads (Last 6 weeks)14

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Campos CKappelhof RCarrilho JMidão LHenriques DCosta E(2024)Role of twinnings in scaling up innovative solutions across EuropeBMJ Innovations10.1136/bmjinnov-2023-001141(bmjinnov-2023-001141)Online publication date: 15-Mar-2024
https://doi.org/10.1136/bmjinnov-2023-001141
Lee GSun A(2021)Understanding the stability of medical concept embeddingsJournal of the Association for Information Science and Technology10.1002/asi.2441172:3(346-356)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24411
Schultheiß SLewandowski D(2021)How users' knowledge of advertisements influences their viewing and selection behavior in search enginesJournal of the Association for Information Science and Technology10.1002/asi.2441072:3(285-301)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24410
Yanovsky SHoernle NLev OGal K(2021)One size does not fit allJournal of the Association for Information Science and Technology10.1002/asi.2440972:3(331-345)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24409
Yigit‐Sert SAltingovde IMacdonald COunis IUlusoy Ö(2021)Explicit diversification of search results across multiple dimensions for educational searchJournal of the Association for Information Science and Technology10.1002/asi.2440372:3(315-330)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24403
Thelwall MSud P(2021)Do new research issues attract more citations? A comparison between 25 Scopus subject categoriesJournal of the Association for Information Science and Technology10.1002/asi.2440172:3(269-279)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1002/asi.24401
Skryzalin JField RFisher ABauer T(2019)Temporal Methods to Detect Content-Based Anomalies in Social MediaFrom Security to Community Detection in Social Networking Platforms10.1007/978-3-030-11286-8_10(213-230)Online publication date: 10-Apr-2019
https://doi.org/10.1007/978-3-030-11286-8_10
Middleton SKordopatis-Zilos GPapadopoulos SKompatsiaris Y(2018)Location Extraction from Social MediaACM Transactions on Information Systems10.1145/320266236:4(1-27)Online publication date: 13-Jun-2018
https://dl.acm.org/doi/10.1145/3202662
Li LZha H(2018)Energy Usage Behavior Modeling in Energy Disaggregation via Hawkes ProcessesACM Transactions on Intelligent Systems and Technology10.1145/31084139:3(1-22)Online publication date: 29-Jan-2018
https://dl.acm.org/doi/10.1145/3108413
Omizo RHart-Davidson WAndersen R(2017)Digging text vizProceedings of the 35th ACM International Conference on the Design of Communication10.1145/3121113.3121221(1-13)Online publication date: 11-Aug-2017
https://dl.acm.org/doi/10.1145/3121113.3121221
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

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

Cited By

Index Terms

Recommendations

Significant motifs in time series

Extracting lexical knowledge from dictionary text

Sentence Features Fusion for Text Summarization Using Fuzzy Logic

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Other Metrics

Article Metrics

Other Metrics

Cited By

PDF

eReader

Login options

Full Access

Abstract

References

Cited By

Index Terms

Recommendations

Significant motifs in time series

Extracting lexical knowledge from dictionary text

Sentence Features Fusion for Text Summarization Using Fuzzy Logic

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Share

Share this Publication link

Share on social media

Affiliations