research of chinese text classification methods based on semantic vector and semantic similarity

ISCAS OpenIR > 2009年期刊/会议论文

	research of chinese text classification methods based on semantic vector and semantic similarity
	Song Xin; Huang Jia; Zhou Jing-Min; Chen Xi
	2009
会议名称	2009 International Forum on Computer Science-Technology and Applications, IFCSTA 2009
会议录名称	IFCSTA 2009 Proceedings - 2009 International Forum on Computer Science-Technology and Applications
页码	187-190
会议日期	40879
会议地点	Chongqing, China
收录类别	ei
出版地	United States
ISBN	9780769539300
部门归属	(1) State Key Laboratory of Software Development Environment, Beihang University, 100191, Beijing, China; (2) Institute of Software Chinese Academy of Sciences, 100190, Beijing, China
摘要	To overcome the limitations of traditional text classification approaches based on bag-of-words representation and to effectively incorporate linguistic knowledge and conceptual index into text vector space model, based on two thesaurus HowNet and Tongyici Cilin(hereinafter referred to Cilin), we use semantic vector to describe a document instead of traditional keywords vector, which is based on merging words with high similarity and using a concept to describe the semantic feature rather than a series of words. It not only reduces feature dimension but also adds semantic information to the vector. We also use sentence (document) similarity based on simple vector distance to classify the text and three groups of experiments are made respectively. The results show that the accuracy rates are generally improved along with semantic treatment, which indicates that semantic mining is very important and necessary to text classification. © 2009 IEEE.
关键词	Computer Science Information Retrieval Systems Knowledge Representation Semantics Vector Spaces Vectors
主办者	IITAA - International Information Technology; and Applications Association
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8434
专题	2009年期刊/会议论文
推荐引用方式 GB/T 7714	Song Xin,Huang Jia,Zhou Jing-Min,et al. research of chinese text classification methods based on semantic vector and semantic similarity[C]. United States,2009:187-190.