%0 Journal Article
%T Research on Automatic Text Classification Based on a Hybrid Language Model<br>基于一种混合语言模型的自动文本分类技术研究
%A Zheng De-quan
%A Li Sheng
%A Zhao Tie-jun
%A Yu Hao
%A <br>郑德权
%A 李生
%A 赵铁军
%A 于浩
%J 电子与信息学报
%D 2007
%I 
%X With the volume of information available increase, text classification has become one of the key on the Internet and corporate intranets continues to technology in organizing and processing large amount of document data. This paper gives a novel method of Chinese text categorization based on a combination of ontology with statistical method. In this study, first, linguistic ontology knowledge bank will be respectively acquired by learning training corpus for various classes to determine the various categorizations. For a actual document, the evaluation value will respectively be gotten by various linguistic ontology knowledge bank and the categorization will be judged by the highest evaluation value. This method is compared with Bayes, k-nearest neighbor and support vector machine, The primary experimental results show that the method outperforms that previous work.
%K Text classification
%K Ontology
%K Hybrid language model
%K Context
%K Multi-grams<br>文本分类
%K 本体
%K 混合语言模型
%K 上下文
%K 多元信息
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=B7047BC5F65EFE6F&yid=A732AF04DDA03BB3&vid=771469D9D58C34FF&iid=38B194292C032A66&sid=ED9DF3402785F68D&eid=10A39635766FF5D0&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=17