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.
|Number of pages||5|
|Journal||Proceedings of the International Conference on Document Analysis and Recognition, ICDAR|
|Publication status||Published - Dec 11 2013|
|Event||12th International Conference on Document Analysis and Recognition, ICDAR 2013 - Washington, DC, United States|
Duration: Aug 25 2013 → Aug 28 2013
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition