skip to main content
10.1145/1390334.1390546acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
poster

Improving text classification accuracy using topic modeling over an additional corpus

Published: 20 July 2008 Publication History

Abstract

The World Wide Web has many document repositories that can act as valuable sources of additional data for various machine learning tasks. In this paper, we propose a method of improving text classification accuracy by using such an additional corpus that can easily be obtained from the web. This additional corpus can be unlabeled and independent of the given classification task. The method proposed here uses topic modeling to extract a set of topics from the additional corpus. Those extracted topics then act as additional features of the data of the given classification task. An evaluation on the RCV1 dataset shows significant improvement over a baseline method.

References

[1]
Blei, D. M., Ng, A. Y., and Jordan, M. J. 2003. Latent Dirichlet allocation. In the Journal of Machine Learning Research, 3:993--1022
[2]
Gabrilovich, E., Markovitch, S. 2006. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. In the proc of 21st National Conference on Artificial Intelligence (AAAI)
[3]
Griffiths, T. L., and Steyvers, M. 2004. Finding Scientific Topics. National Academy of Sciences. 5228--5235
[4]
Fei-Fei, L. and Perona, P. 2005. A Bayesian Hierarchical Model for Learning Natural Scene Categories. In the proc of Computer Vision and Pattern Recognition (CVPR)
[5]
Lewis, D., Yang, Y., Rose, T., and Li, F. 2004. RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 5:361--397

Cited By

View all
  • (2014)Name Entity Conflict Detection in Biomedical Text Data Based on Probabilistic Topic ModelsProceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies10.1145/2677855.2677916(1-6)Online publication date: 14-Nov-2014
  • (2013)Improving semi-supervised text classification by using wikipedia knowledgeProceedings of the 14th international conference on Web-Age Information Management10.1007/978-3-642-38562-9_3(25-36)Online publication date: 14-Jun-2013
  • (2012)Non-Topical Classification of Query Logs Using Background KnowledgeMachine Learning10.4018/978-1-60960-818-7.ch314(598-615)Online publication date: 2012
  • Show More Cited By

Index Terms

  1. Improving text classification accuracy using topic modeling over an additional corpus

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
      July 2008
      934 pages
      ISBN:9781605581644
      DOI:10.1145/1390334
      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

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 20 July 2008

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. semi-supervised learning
      2. text classification
      3. topic modeling

      Qualifiers

      • Poster

      Conference

      SIGIR '08
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 792 of 3,983 submissions, 20%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)8
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 15 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2014)Name Entity Conflict Detection in Biomedical Text Data Based on Probabilistic Topic ModelsProceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies10.1145/2677855.2677916(1-6)Online publication date: 14-Nov-2014
      • (2013)Improving semi-supervised text classification by using wikipedia knowledgeProceedings of the 14th international conference on Web-Age Information Management10.1007/978-3-642-38562-9_3(25-36)Online publication date: 14-Jun-2013
      • (2012)Non-Topical Classification of Query Logs Using Background KnowledgeMachine Learning10.4018/978-1-60960-818-7.ch314(598-615)Online publication date: 2012
      • (2011)Non-Topical Classification of Query Logs Using Background KnowledgeMachine Learning Techniques for Adaptive Multimedia Retrieval10.4018/978-1-61692-859-9.ch009(194-212)Online publication date: 2011
      • (2011)Multi-classification of business types on twitter based on topic modelThe 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 201110.1109/ECTICON.2011.5947886(508-511)Online publication date: May-2011
      • (2010)Unsupervised Feature Generation using Knowledge Repositories for Effective Text CategorizationProceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence10.5555/1860967.1861225(1101-1102)Online publication date: 4-Aug-2010
      • (2010)Collaborative future event recommendationProceedings of the 19th ACM international conference on Information and knowledge management10.1145/1871437.1871542(819-828)Online publication date: 26-Oct-2010

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media