一种改进的贝叶斯网络短文本分类算法  

An Improved Short Text Classification Algorithm Based on Bayesian Network

作　　者：郭泗辉[1] 樊兴华[1] 

机构地区：[1]重庆邮电大学计算机科学与技术研究所,重庆400065

出　　处：《广西师范大学学报（自然科学版）》2010年第3期140-143,共4页Journal of Guangxi Normal University:Natural Science Edition

基　　金：国家自然科学基金资助项目(60703010);重庆市自然科学基金资助项目(CSTC2009BB2079)

摘　　要：对于短文本由于其特征数少而使分类效果不理想的情况,提出一种改进的贝叶斯网络文本分类算法,改进之处在于贝叶斯网络结构学习阶段寻找父节点的步骤,算法在考虑了连接强度的因素后,减少了本来不相关的两个节点被归类为父子关系的错误干扰,使每个节点找到的父节点更加准确,从而使文本的分类准确度得到了提升。Due to the fact that inadequate features of short text makes unsatisfactory classification ,an improved algorithm for text classification based on Bayesian network is presented. The improvement lies in the steps of Bayesian network structure learning phase for finding parent node. The algorithm takes into account the connection strength of the factors,thus,a decrease of interference errors between two nodes that are unrelated to the father and son nodes is achieved. It enables each node to find more accurate father node ,and improves the classification accuracy of text.

关 键 词：贝叶斯网络 连接强度 短文本分类 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]