Skip to main content
SpringerLink
Log in

Theme-Based Retrieval of Web News

  • Conference paper
  • First Online:
The World Wide Web and Databases (WebDB 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1997))

Included in the following conference series:

Abstract

We introduce an information system for organization and retrieval of news articles from Web publications, incorporating a classification framework based on Support Vector Machines. We present the data model for storage and management of news data and the system architecture for news retrieval, classification and generation of topical collections. We also discuss the classification results obtained with a collection of news articles gathered from a set of online newspapers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allan, J., Papka, R. and Lavrenko V.. On-line New Event Detection and Tracking. In Proceedings of the 21th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’98), pages 37–45, 1998.

    Google Scholar 

  2. Bowman, C., Danzig, P., Hardy, D., Manber, U. and Schwartz, M.. The Harvest Information Discovery and Access System. In Proceedings of the Second International WWW Conference. pp.763–771, 1994.

    Google Scholar 

  3. Dumais, S., Platt, J., Heckerman, D. and Sahami, M., Inductive Learning Algorithms and Representations for Text Categorization. Proceedings of the Seventh International Conference on Information and Knowledge Management, 1998.

    Google Scholar 

  4. Joachims, T., Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the Tenth European Conference on Machine Learning-ECML, 1998.

    Google Scholar 

  5. Joachims, T., Making large-Scale SVM Learning Practical. Advances in Kernel Methods-Support Vector Learning, B. Schölkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999.

    Google Scholar 

  6. Manber, U., and Wu, S.. Glimpse: a tool to search through entire file systems. In Proceedings of the USENIX Winter Conference, pages 23–32, 1994.

    Google Scholar 

  7. Maria, N., Gaspar, P., Grilo, N., Ferreira, A. and Silva M. J.. ARIADNE-Digital Library Architecture. In Proceedings of the 2nd European Conference on digital Libraries (ECDL’98), pages 667–668, 1998.

    Google Scholar 

  8. Vapnik, V. N.. The Nature of Statistical Learning Theory. Springer, New York, 1995.

    MATH  Google Scholar 

  9. Yang, Y. and Liu X.. A re-examination of text categorization methods. In Proceedings of the 22th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), pages 42–49, 1999.

    Google Scholar 

  10. Yan, T. and Garcia-Molina, H.. SIFT-A Tool for Wide-Area Information Dissemination. In Proceedings of the 1995 Usenix Technical Conference, pages 177–86, 1995.

    Google Scholar 

  11. Yang, Y., Carbonell, J., Brown, R., Pierce, T., Archibald B. T. and Liu X.. Learning approaches for Detecting and Tracking News Events. IEEE Intelligent Systems: Special Issue on Applications of Intelligent Information Retrieval, Vol. 14(4), pages 32–43, July/August 1999.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DI/FCUL Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisboa, Portugal

    Nuno Maria & Mário J. Silva

Authors
  1. Nuno Maria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mário J. Silva
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Universität Münster, Wirtschaftsinformatik Steinfurter Str.109, 48149, Münster, Germany

    Gerhard Goos

  2. Karlsruhe University, Germany

    Juris Hartmanis

  3. Cornell University, NY, USA

    Jan van Leeuwen

  4. Utrecht University, The Netherlands

    Dan Suciu

  5. Computer Science and Engineering, University ofWashington, WA 98195-2350, Seattle, USA

    Gottfried Vossen

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maria, N., Silva, M.J. (2001). Theme-Based Retrieval of Web News. In: Goos, G., Hartmanis, J., van Leeuwen, J., Suciu, D., Vossen, G. (eds) The World Wide Web and Databases. WebDB 2000. Lecture Notes in Computer Science, vol 1997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45271-0_2

Download citation

  • DOI: https://doi.org/10.1007/3-540-45271-0_2

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41826-9

  • Online ISBN: 978-3-540-45271-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation