An Algorithm of Feature Selection and Feature Weighting Adjustment Based on Chinese FrameNet

The combination of TF and DF, which is used as the method of feature selection, and TF-EDF algorithm, which is used as feature weighting, are frequently used in the text categorization. But for a small training set, the combination of TF and DF will filter out many low-frequency words which have a strong capability of the feature discrimination. Hence the weight is directly influenced. In this paper, an algorithm of feature selection and feature weighting adjustment based on Chinese FrameNet (CFN) are presented which aims at solving the problem mentioned above. The experimental result indicates that the precision which is greater than the traditional algorithm can reach to 67.3% and can fits the small training set very well.