An Improved Class-Center Method for Text Classification Using Dependencies and WordNet

Abstract

Automatic text classification is a research focus and core technology in natural language processing and information retrieval. The class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse; its classification accuracy is not high and it lacks semantic information. To overcome these problems, this paper proposes an improved class-center method for text classification using dependencies and the WordNet dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on a large corpus.