基于向量空间模型的文本分类系统的研究与实现

文本分类是信息处理的一个重要的研究课题,它可以有效的解决信息杂乱的现象并有助于定位所需的信息。本文综合考虑了频度、分散度和集中度等几项测试指标,提出了一种新的特征抽取算法,克服了传统的从单一或片面的测试指标进行特征抽取所造成的特征“过度拟合”问题,并基于此实现了二级分类模式的文本分类系统。和类中心分类法相比,实验结果表明二级分类模式具有较高的精度和召回率。

Research and Implementation of Text Classification System Based on VSP

Text classification is an important research task of natural language processing , which can efficiently resolve the issue of information chaos and help to locate the required information. The traditional approaches of text classification commonly extract feature terms from a single test criterion , which will lead to the problemof“over fitting”. This paper comprehensively takes test criterions such as frequency , distribution and concentration into account and proposes a new arithmetic of feature extraction and implements text classification systemwith two-level mode. The experimental results show that two-level classification mode has higher classification precision and recall compared with center classification method.