%0 Conference Proceedings
%T A Laplacian Eigenmaps Based Semantic Similarity Measure between Words
%+ Key Laboratory of Intelligent Information Processing, Institute of Computing Technology [Beijing]
%+ Graduate University of Chinese [Beijing] (UCAS)
%A Wu, Yuming
%A Cao, Cungen
%A Wang, Shi
%A Wang, Dongsheng
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP)
%C Manchester, United Kingdom
%Y Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake
%I Springer
%3 Intelligent Information Processing V
%V AICT-340
%P 291-296
%8 2010-10-13
%D 2010
%R 10.1007/978-3-642-16327-2_35
%Z Computer Science [cs]/Digital Libraries [cs.DL]Conference papers
%X 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%.
%G English
%2 https://inria.hal.science/hal-01060365/document
%2 https://inria.hal.science/hal-01060365/file/WuCWW10.pdf
%L hal-01060365
%U https://inria.hal.science/hal-01060365
%~ IFIP
%~ IFIP-AICT
%~ IFIP-AICT-340
%~ IFIP-TC
%~ IFIP-TC12
%~ IFIP-IIP