Chinese Text Categorization Based on Deep Belief Networks

With the rapid development of Internet, text categorization becomes a mission-critical technology that organizes and processes large amounts of data in document. Deep belief networks have powerful abilities of learning and can extract highly distinguishable features from the high-dimensional original feature space. So a new Chinese text categorization algorithm based on deep learning structure and semi-supervised deep belief networks is presented in this paper. We extract original feature with TFIDF-ICF, construct the text classification model based on DBN, and select the number of hidden layers and hidden units. Our experimental results indicated that the performance of text categorization algorithm based on deep belief networks is better than support vector machine.