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Fast Text Categorization Based on a Novel Class Space Model

  • Yingfan Gao
  • Runbo Ma
  • Yushu Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

Automatic categorization has been shown to be an accurate alternative to manual categorization in which documents are processed and automatically assigned to pre-defined categories. The accuracy of different methods for categorization has been studied largely, but their efficiency has seldom been mentioned. Aiming to maintain effectiveness while improving efficiency, we proposed a fast algorithm for text categorization and a compressed document vector representation method based on a novel class space model. The experiments proved our methods have better efficiency and tolerable effectiveness.

Keywords

Test Document Text Classification Vector Space Model Training Corpus Document Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yingfan Gao
    • 1
  • Runbo Ma
    • 2
  • Yushu Liu
    • 1
  1. 1.School of Computer Science & TechnologyBeijing Institute of TechnologyBeijingP.R. China
  2. 2.College of Physics and ElectronicsShanxi UniversityTaiyuanP.R. China

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