Skip to main content
Springer Nature Link
Log in

Short-Text Classification Based on ICA and LSA

  • Conference paper
Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

Included in the following conference series:

Abstract

Many applications, such as word-sense disambiguation and information retrieval, can benefit from text classification. Text classifiers based on Independent Component Analysis (ICA) try to make the most of the independent components of text documents and give in many cases good classification effects. Short-text documents, however, usually have little overlap in their feature terms and, in this case, ICA can not work well. Our aim is to solve the short-text problem in text classification by using Latent Semantic Analysis (LSA) as a data preprocessing method, then employing ICA for the preprocessed data. The experiment shows that using ICA and LSA together rather than only using ICA in Chinese short-text classification can provide better classification effects.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • Honkela, T., Hyvärinen, A.: Linguistic Feature Extraction Using Independent Component Analysis. In: Proc. Int. Joint Conf. on Neural Networks (IJCNN), Budapest, Hungary (2004)

    Google Scholar 

  • Sevillano, X., Alías, F., Socoró, J.C.: Reliability in ICA-based Text Classification. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 1213–1220. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  • Kolenda, T., Hansen, L.K.: Independent Components in Text. Advances in Neural Information Processing Systems 13, 235–256 (2000)

    Google Scholar 

  • Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)

    MATH  Google Scholar 

  • Landauer, T.K., Foltz, P.W., Laham, D.: Introduction to Latent Semantic Analysis. Discourse Processes 25, 259–284 (1998)

    Article  Google Scholar 

  • Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science 41, 391–407 (1990)

    Article  Google Scholar 

  • Hyvärinen, A.: Survey on Independent Component Analysis. Neural Computing Surveys 2, 94–128 (1999)

    Google Scholar 

  • Isbell, C.L., Viola, P.: Restructuring Sparse High Dimensional Data for Effective Retrieval. Advances in Neural Information Processing Systems 11, 480–486 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Qiang Pu & Guo-Wei Yang

Authors
  1. Qiang Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guo-Wei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pu, Q., Yang, GW. (2006). Short-Text Classification Based on ICA and LSA. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_39

Download citation

  • DOI: https://doi.org/10.1007/11760023_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics