A Study on Automatic Chinese Text Classification

In this paper, we perform Chinese text classification using N-gram (uni-gram, bi-gram and mixed uni-gram/bi-gram) frequency feature instead of word frequency feature to represent documents and propose the use of mixed uni-gram/bi-gram after feature transformation. We further propose 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. Furthermore, we present several experiments evaluating the selection of features based on part-of-speech analysis and the results show that suitable combination of part-of-speech can lead to better classification performance.