Chapter

Intelligent Information Processing V

Volume 340 of the series IFIP Advances in Information and Communication Technology pp 291-296

A Laplacian Eigenmaps Based Semantic Similarity Measure between Words

  • Yuming WuAffiliated withKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of SciencesGraduate University of Chinese Academy of Sciences
  • , Cungen CaoAffiliated withKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
  • , Shi WangAffiliated withKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences
  • , Dongsheng WangAffiliated withKey Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of SciencesGraduate University of Chinese Academy of Sciences

Abstract

The measurement of semantic similarity between words is very important in many applicaitons. In this paper, we propose a method based on Laplacian eigenmaps to measure semantic similarity between words. First, we attach semantic features to each word. Second, a similarity matrix ,which semantic features are encoded into, is calculated in the original high-dimensional space. Finally, with the aid of Laplacian eigenmaps, we recalculate the similarities in the target low-dimensional space. The experiment on the Miller-Charles benchmark shows that the similarity measurement in the low-dimensional space achieves a correlation coefficient of 0.812, in contrast with the correlation coefficient of 0.683 calculated in the high-dimensional space, implying a significant improvement of 18.9%.