计算机工程2016，Vol.42Issue(12)：181-187,7.DOI:10.3969/j.issn.1000-3428.2016.12.032
基于概念聚类的领域本体图中文文本分类
Chinese Text Classification by Domain Ontology Graph Based on Concept Clustering
摘要
Abstract
This paper proposes an improved Chinese text classification algorithm by Domain Ontology Graph(DOG)based on semi-supervised concept clustering.According to the DOG structure model,the ontology learning framework of Chinese classification is created,and then the HowNet dictionary is used to extract the word disambiguation,and then the Chinese term-term relationship mapping is established.Based on the weight connection between the terms of the binary classification relationship,here designs the KLSeeker ontology Chinese text classification algorithm.It realizes accurate classification of Chinese text through DOG semi-supervised learning.Experimental results show that the proposed algorithm has a higher classification accuracy compared with the Chinese text classification algorithm based on non-negative tensor decomposition ontology concepts.关键词
词消歧/半监督/概念聚类/HowNet字典/二分类关系/领域本体图Key words
word disambiguation/semi-supervised/concept clustering/HowNet dictionary/binary classification relationship/Domain Ontology Graph(DOG)分类
信息技术与安全科学引用本文复制引用
叶施仁,孙宁..基于概念聚类的领域本体图中文文本分类[J].计算机工程,2016,42(12):181-187,7.基金项目
国家自然科学基金(61272367). （61272367）
