Fast text categorization based on collaborative work in the semantic and class spaces

Abstract

The blooming of the Internet information has made fast text categorization very essential. Generally, in order to accelerate the classification process, the classifier needs to be simplified as much as possible; however, the accuracy might descend drastically in that case, This paper proposes a novel approach to achieve a suitable tradeoff between the speed and accuracy. With category information fusion and basis orthogonality non-negative matrix factorization, the documents can be mapped from the term space to a semantic or class s-pace, and a simple and fast classification method in the class space is proposed. Furthermore a criterion for re-classifying in the semantic space is discussed. Finally, the collaborative work framework in the semantic and class spaces is implemented. Experiments in two benchmarks are presented, and the results are encouraging.