A Laplacian Eigenmaps Based Semantic Similarity Measure between Words

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%.
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Communication dans un congrès
Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.291-296, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_35〉
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Yuming Wu, Cungen Cao, Shi Wang, Dongsheng Wang. A Laplacian Eigenmaps Based Semantic Similarity Measure between Words. Zhongzhi Shi; Sunil Vadera; Agnar Aamodt; David Leake. 6th IFIP TC 12 International Conference on Intelligent Information Processing (IIP), Oct 2010, Manchester, United Kingdom. Springer, IFIP Advances in Information and Communication Technology, AICT-340, pp.291-296, 2010, Intelligent Information Processing V. 〈10.1007/978-3-642-16327-2_35〉. 〈hal-01060365〉

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