Abstract：When n-grams are considered as text classification features, the classification accuracy is decreased. The redundancy and relevance between words are ignored while n-grams are weighted. Thus, n-grams features weighting algorithm based on relevance and semantic is proposed. To decrease the internal redundancy, feature reduction is conducted to n-grams during text preprocessing. Then, n-grams are weighted according to the relevance of words and classes in n-grams and the semantic similarity of n-grams and testing dataset. The experimental results on Sougo Chinese news corpse and NetEase text corpse show that the proposed algorithm can select n-grams features of high relevance and low redundancy, and reduce the sparse data while quantifying the testing dataset.
邱云飞，刘世兴，林明明，邵良杉. 基于相关性及语义的n-grams特征加权算法*[J]. 模式识别与人工智能, 2015, 28(11): 992-1001.
QIU Yun-Fei, LIU Shi-Xing, LIN Ming-Ming, SHAO Liang-Shan. n-grams Features Weighting Algorithm Based on Relevance and Semantic. , 2015, 28(11): 992-1001.
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2015, Vol. 28 
