Similarity measure based on improved optimal assignment model

doi:10.1109/IHMSC.2010.39

	Similarity measure based on improved optimal assignment model
	Zhang, Yong; Deng, Ke
	2010
会议录名称	Proceedings - 2010 2nd International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2010
卷号	1
页码	125-128
出版者	IEEE Computer Society
摘要	Measuring similarity has a wide range of application in information retrieval, machine translation or other related fields. In this paper, we proposed a text similarity computation based on improved optimal assignment model, which combine the improved Hungarian algorithm with the semantic similarity of terms to obtain the maximum semantic similarity between two documents or between a query and a document. Experiment shows that the algorithm has a significant improvement for semantic similarity comparing to traditional models of similarity measure. the method can be applied to document clustering, which will enchance the accuracy of result. © 2010 IEEE.
关键词	Document Clustering Hungarian algorithm Machine translations Measuring similarities Optimal assignment Semantic similarity Similarity measure Traditional models
DOI	10.1109/IHMSC.2010.39
收录类别	EI
语种	英语
EI入藏号	20104713407123
EI主题词	Semantics
来源库	Compendex
分类代码	716.1 Information Theory and Signal Processing - 723 Computer Software, Data Handling and Applications - 751.5 Speech - 921 Mathematics - 921.5 Optimization Techniques
引用统计	无
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/116067
专题	兰州理工大学
作者单位	College of Computer and Communication, LanZhou University of Technology, Lanzhou, China
第一作者单位	兰州理工大学
推荐引用方式 GB/T 7714	Zhang, Yong,Deng, Ke. Similarity measure based on improved optimal assignment model[C]:IEEE Computer Society,2010:125-128.