Similarity Measure Based on Improved Optimal Assignment Model

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.

关键词： semantic similarity optimal assignment mode Hungarian algorithm

作者: Yong Zhang Ke Deng

作者单位: College of Computer and Communication LanZhou University of Technology LanZhou,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 125-128

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）