Automatic Chinese Text Classification Using Character-based and Word-based Approach

被引：4

作者：

Luo, Xi ^{[1
]}

Ohyama, Wataru ^{[1
]}

Wakabayashi, Tetsushi ^{[1
]}

Kimura, Fumitaka ^{[1
]}

机构：

[1] Mie Univ, Grad Sch Engn, Tsu, Mie 514, Japan

来源：

2013 12TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR) | 2013年

关键词：

Chinese Text Classification/Categorization; N-gram; Feature Transformation; Dimension Reduction; Principal Component Analysis; Support Vector Machine;

D O I：

10.1109/ICDAR.2013.73

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：329 / 333

页数：5

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