基于概念的文本类别特征提取与文本模糊匹配  

The Feature Extraction of Text Category and Text Fuzzy Matching Based on Concept

作　　者：罗三定[1] 陆文彦[1] 王浩[1] 贾维嘉[2] 

机构地区：[1]中南大学信息科学与工程学院,长沙410083 [2]香港城市大学电脑工程与信息技术系

出　　处：《计算机工程与应用》2002年第16期97-99,104,共4页Computer Engineering and Applications

基　　金：国家海外杰出青年自然科学基金(编号:699282201);香港城市大学计划项目(编号:9640006)

摘　　要：文本信息特征提取和文本分类是当前智能信息服务系统基础研究的重点。该文给出一种新的类别特征提取与文本匹配方法。首先对术语特征权进行了综合计算,然后基于概念网络术语—概念映射关系,将特征权由术语空间转换到概念空间并做权值限幅处理。在此基础上,通过对概念进行类内和类间的统计分析,得到类别特征的均值与方差两个向量,通过模糊距离计算来对文本进行类别匹配。该文方法克服了传统IDF方法缺点,能有效地从概念上提取文本类特征,提高文本自动分类的准确性。Text feature extraction and text categorization is the focal point of basic research in the field of intelligent information service system.A novel method of category feature extraction and text fuzzy matching is presented in this paper.On the basis of comprehensive calculation,conversion from term space to concept space using term-concept mapping table of concept network and amplitude limiting procession on feature weight ,a statistic analysis is processed within categories and between categories.The category feature is then signified by two vectors:mean value and standard deviation.Furthermore,the category matching of text is implemented by using fuzz distance calculation.This new method eliminates the drawbacks of traditional IDF method and it can efficiently conduct text feature extraction thus to promote the accuracy of automation text categorization.

关 键 词：概念 文本类别 特征提取 文本模糊匹配 信息处理 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]