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Forgetting Word Segmentation in Chinese Text Classification with L1-Regularized Logistic Regression

  • Qiang Fu
  • Xinyu Dai
  • Shujian Huang
  • Jiajun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

Abstract

Word segmentation is commonly a preprocessing step for Chinese text representation in building a text classification system. We have found that Chinese text representation based on segmented words may lose some valuable features for classification, no matter the segmented results are correct or not. To preserve these features, we propose to use character-based N-gram to represent the Chinese text in a larger scale feature space. Considering the sparsity problem of the N-gram data, we suggest the L1-regularized logistic regression (L1-LR) model to classify Chinese text for better generalization and interpretation. The experimental results demonstrate our proposed method can get better performance than those state-of-the-art methods. Further qualitative analysis also shows that character-based N-gram representation with L1-LR is reasonable and effective for text classification.

Keywords

Text classification Text representation Chinese Character-based N-gram L1-regularized logistic regression 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Qiang Fu
    • 1
  • Xinyu Dai
    • 1
  • Shujian Huang
    • 1
  • Jiajun Chen
    • 1
  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

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