TEXT CLASSIFICATION BASED ON A COMBINATION OF ONTOLOGY WITH STATISTICAL METHOD

摘要：

Text classification is becoming one of the key techniques in organizing and handling a large amount of text data. In this paper, a combination of ontology with statistical method is presented to improve the precision of text classification. In this study, first, different kind of linguistic ontology knowledge will be respectively acquired by learning training corpus to determine text classifiers. For an actual document,the semantic evaluation value of the document will respectively be gotten by different kind of linguistic ontology knowledge and the categories will be judged by the highest evaluation value. Compared with Bayes, k-nearest neighbor and support vector machine, the proposed approach outperforms previous works.

关键词： Text classification ontology statistical method linguistic ontology knowledge

作者: FENG YU DE-QUAN ZHENG TIE-JUN ZHAO SHENG LI HAO YU

作者单位: School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150076, China School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150076, China; MOE-MS Key Laboratory of Natural Language Processing and Speech, Harbin Institute of Technology,Harb

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1042-1047

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）