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Using Wikipedia knowledge to improve text classification

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Abstract

Text classification has been widely used to assist users with the discovery of useful information from the Internet. However, traditional classification methods are based on the “Bag of Words” (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. To overcome this problem, previous work attempted to enrich text representation by means of manual intervention or automatic document expansion. The achieved improvement is unfortunately very limited, due to the poor coverage capability of the dictionary, and to the ineffectiveness of term expansion. In this paper, we automatically construct a thesaurus of concepts from Wikipedia. We then introduce a unified framework to expand the BOW representation with semantic relations (synonymy, hyponymy, and associative relations), and demonstrate its efficacy in enhancing previous approaches for text classification. Experimental results on several data sets show that the proposed approach, integrated with the thesaurus built from Wikipedia, can achieve significant improvements with respect to the baseline algorithm.

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Authors and Affiliations

  1. Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA

    Pu Wang

  2. Machine Learning Group, Microsoft Research Asia, Beijing, China

    Jian Hu, Hua-Jun Zeng & Zheng Chen

Authors
  1. Pu Wang
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  2. Jian Hu
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  3. Hua-Jun Zeng
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  4. Zheng Chen
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Correspondence to Pu Wang.

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Wang, P., Hu, J., Zeng, HJ. et al. Using Wikipedia knowledge to improve text classification. Knowl Inf Syst 19, 265–281 (2009). https://doi.org/10.1007/s10115-008-0152-4

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  • DOI: https://doi.org/10.1007/s10115-008-0152-4

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