Heterogeneous Information Integration in Hierarchical Text Classification

  • Huai-Yuan Yang
  • Tie-Yan Liu
  • Li Gao
  • Wei-Ying Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3918)


Previous work has shown that considering the category distance in the taxonomy tree can improve the performance of text classifiers. In this paper, we propose a new approach to further integrate more categorical information in the text corpus using the principle of multi-objective programming (MOP). That is, we not only consider the distance between categories defined by the branching of the taxonomy tree, but also consider the similarity between categories defined by the document/term distributions in the feature space. Consequently, we get a refined category distance by using MOP to leverage these two kinds of information. Experiments on both synthetic and real-world datasets demonstrated the effectiveness of the proposed algorithm in hierarchical text classification.


Singular Value Decomposition Classification Performance Synthetic Dataset Taxonomy Tree Text Corpus 
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

  • Huai-Yuan Yang
    • 1
    • 2
  • Tie-Yan Liu
    • 1
  • Li Gao
    • 2
  • Wei-Ying Ma
    • 1
  1. 1.5F Sigma CenterMicrosoft Research AsiaBeijingP.R. China
  2. 2.Department of Scientific & Engineering Computing School of Mathematical SciencesPeking UniversityBeijingP.R. China

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