Automatic chinese text classification using character-based and word-based approach

Xi Luo, Wataru Ohyama, Tetsushi Wakabayashi, Fumitaka Kimura

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)


In this paper, we study on Chinese text classification using character-based approach (N-gram) and word-based approach and propose the use of uni-gram, bi-gram and word features of length greater than or equal to three. A weight coefficient which can be used to give higher weights to word features is also introduced. We further investigate a serial approach based on feature transformation and dimension reduction techniques to improve the performance. Experimental results show that our proposed approach is efficient and effective for improving the performance of Chinese text classification.

Original languageEnglish
Article number6628638
Pages (from-to)329-333
Number of pages5
JournalProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Publication statusPublished - 2013
Externally publishedYes
Event12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States
Duration: Aug 25 2013Aug 28 2013

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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