A high performance prototype system for chinese text categorization

How to improve the accuracy of categorization is a big challenge in text categorization. This paper proposes a high performance prototype system for Chinese text categorization, which mainly includes feature extraction subsystem, feature selection subsystem, and reliability evaluation subsystem for classification results. The proposed prototype system employs a two-step classifying strategy. First, the features that are effective for all testing texts are used to classify texts. Then, the reliability evaluation subsystem evaluates the classification results directly according to the outputs of the classifier, and divides them into two parts: texts classified reliable or not. Only for the texts classified unreliable at the first step, go to the second step. Second, a classifier uses the features that are more subtle and powerful for those texts classified unreliable to classify the texts. The proposed prototype system is successfully implemented in a case that exploits a Naive Bayesian classifier as the classifier in the first and second steps. Experiments show that the proposed prototype system achieves a high performance.