【摘要】 在采用VSM模型进行文本分类时,如果特征向量维数相差悬殊,会给分类结果产生很大负面影响。为了解决这一问题,本文引入了特征向量扩展的思想,同时定义了有效原始信息浓度的概念。特征向量扩展以HowNet语义词典为依据,对高维和低维特征向量采用不同的扩展策略,从而减小了不同类别语料间有效原始信息浓度的差值,进而改善复杂语料的分类结果。实验表明该方法在复杂语料情况下,通过对特征向量进行HowNet语义扩展,可以较好的改善分类结果。更多还原

【Abstract】 The disproportion of dimensions of class vectors brings troubles to text categorization by VSM,so we introduce the idea of vector expansion and define the Concentration of Effective Original Information(CEOInfo)to resolve the problem.Based on HowNet,which is a semantic dictionary,we use different expanding strategies for vectors of high dimensions and low dimensions.This method reduces the margin of CEOInfo among different classes.The experiment shows that the precision of categorization is enhanced by VSM expansion based on HowNet under the condition of the disproportion of dimensions of class vectors.更多还原