A Laplacian Eigenmaps Based Semantic Similarity Measure between Words

  • Yuming Wu
  • Cungen Cao
  • Shi Wang
  • Dongsheng Wang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 340)

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%.

Copyright information

© IFIP International Federation for Information Processing 2010

Authors and Affiliations

  • Yuming Wu
    • 1
    • 2
  • Cungen Cao
    • 1
  • Shi Wang
    • 1
  • Dongsheng Wang
    • 1
    • 2
  1. 1.Key Laboratory of Intelligent Information Processing, Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  2. 2.Graduate University of Chinese Academy of SciencesBeijingChina

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