基于概念簇的文本向量构建方法

Method of text vector construction based on concept cluster

为提高文本向量对文本概念的逼近程度,通过将具有相同语法语义特征的词进行聚类,提取概念簇,利用空间变换将文本向量由词空间变换到概念簇空间上来表达文本。实验比较了基于TF-IDF、IG、TF-IDF-IG、LSA以及它们结合概念簇后对文本分类的效果,证明了基于概念簇的文本向量构建方法能提高文本向量对文本概念逼近的准确程度,同时也提高了不同类型文本之间的区分度。

To enhance the performance of the text vector,terms were clustered,which contained similar syntax or seman-tic feature,to construct concept cluster.The text vector would be transformed from term-space to concept-cluster-space to represent the original text.The experiment compared effects of text classification based on TF-IDF,IG,TF-IDF-IG,LSA,and their combinations with concept cluster.And the results show that,the text vector based on concept cluster improves the accuracy of text concept approaching,and advances the discriminating degree between different types of texts.