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Research on Text Categorization Based on a Weakly-Supervised Transfer Learning Method

  • Dequan Zheng
  • Chenghe Zhang
  • Geli Fei
  • Tiejun Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7182)

Abstract

This paper presents a weakly-supervised transfer learning based text categorization method, which does not need to tag new training documents when facing classification tasks in new area. Instead, we can take use of the already tagged documents in other domains to accomplish the automatic categorization task. By extracting linguistic information such as part-of-speech, semantic, co-occurrence of keywords, we construct a domain-adaptive transfer knowledge base. Relation experiments show that, the presented method improved the performance of text categorization on traditional corpus, and our results were only about 5% lower than the baseline on cross-domain classification tasks. And thus we demonstrate the effectiveness of our method.

Keywords

Transfer learning Text Categorization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dequan Zheng
    • 1
  • Chenghe Zhang
    • 1
  • Geli Fei
    • 1
  • Tiejun Zhao
    • 1
  1. 1.MOE-MS Key Laboratory of Natural Language Processing and SpeechHarbin Institute of TechnologyHarbinChina

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