关键词: 汉语单双字识别, 字矩阵, 交运算, 特征选择, 特征加权

Abstract: In Chinese text, traditional n-grams feature selection and weighting methods（Sliding window method and so on） have two shortages: the word segmentation must be called before words’ combination and n-grams’ generation. The redundancy n-grams disturb other useful n-grams and reduce the precision of classification because of the redundancy words in n-grams that can’t be deleted. To solve the problems, transform the text to single-word matrix according to Chinese single, double word identification theory. Avoid redundancy words existing in n-grams and calling word segmentation to text by redundancy filtering and intersection in single-word matrix. The experiment results in Sogou Chinese news corpus and NetEase text corpus show that compared with sliding window and other methods, the n-grams features using the method of n-grams feature selection and weighting algorithm based on single-word matrix intersection cost less time and behave better in SVM（Support Vector Machine）.

Key words: Chinese single and double word recognition, single-word matrix, intersection, feature selection, feature weighting