【作者】 韩露； 余正涛； 邓锦辉； 章程； 毛存礼； 郭剑毅；

【Author】 Han Lu 1,Yu Zhengtao 1,2,Deng Jinhui 1,Zhang Cheng 1,Mao Cunli 2,Guo Jianyi 1,2 1.The School of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650051,P.R.China 2.The Institute of Intelligent Information Processing,Computer Technology Application Key Laboratory of Yunnan Province,Kunming 650051,P.R.China

【机构】 昆明理工大学信息工程与自动化学院； 云南省计算机技术应用重点实验室智能信息处理研究所；

【摘要】 文本分类通常采用统计方法选取特征,在不同领域之间进行文本分类时,通常不考虑领域内部所具有的特有的知识关系。本文提出了一种基于领域知识关系的领域文本分类模型,该模型采用支持向量机学习算法,结合样本统计与领域术语构造领域分类特征空间,利用领域内部知识关系,计算领域概念间的相关性,从而为分类特征赋予一定权重,实现领域文本分类。并利用该模型进行了云南旅游领域与非旅游领域文本分类实验,结果表明,领域知识关系对领域文本分类有较大影响,分类的准确率比改进TFIDF方法提高4个百分点。更多还原

【Abstract】 The text classification usually uses the statistical method to select characteristic.When it is carried out in different domains,the special interior knowledge relationships between domains will not be considered.In this paper,a new text classi-fication model is proposed,which is based on the domain knowledge relations.This model adopts the support vector machine study algorithm,combine statistic samples and domain terminology to make up classification feature space,and calculate the similarity between domain conceptions,so that classification characteristic is entrusted with certain weight,realizing domain text classification.The new model has been made use of to carry out a text classification experiment about YunNan travel domain and non-travel domain.The result shows that domain knowledge has great effects on domain text classification and the accuracy of classification has been improved by 4 percentage compared with the improved TFIDF method.更多还原