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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Wu, Y., Cao, C., Wang, S., Wang, D. (2010). A Laplacian Eigenmaps Based Semantic Similarity Measure between Words. In: Shi, Z., Vadera, S., Aamodt, A., Leake, D. (eds) Intelligent Information Processing V. IIP 2010. IFIP Advances in Information and Communication Technology, vol 340. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16327-2_35
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DOI: https://doi.org/10.1007/978-3-642-16327-2_35
Publisher Name: Springer, Berlin, Heidelberg
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