Eliminating High-Degree Biased Character Bigrams for Dimensionality Reduction in Chinese Text Categorization

Abstract

High dimensionality of feature space is a main obstacle for Text Categorization (TC). In a candidate feature set consisting of Chinese character bigrams, there exist a number of bigrams which are high-degree biased according to character frequencies. Usually, these bigrams are likely to survive for their strength of discriminating documents after the process of feature selection. However, most of them are useless for document categorization because of the weakness in representing document contents. The paper firstly defines a criterion to identify the high-degree biased Chinese bigrams. Then, two schemes called s-BR1 and s-BR2 are proposed to deal with these bigrams: the former directly eliminates them from the feature set whereas the latter replaces them with the corresponding significant characters involved. Experimental results show that the high-degree biased bigrams should be eliminated from the feature set, and the σ-BR1 scheme is quite effective for further dimensionality reduction in Chinese text categorization, after a feature selection process with a Chi − CIG score function.