Abstract
Chinese word similarity computing is a fundamental task for natural language processing. This paper presents a method to calculate the similarity between Chinese words based on combination strategy. We apply Baidubaike to train Word2Vector model, and then integrate different methods, semantic Dictionary-based method, Word2Vector-based method and Chinese FrameNet (CFN)-based method, to calculate the semantic similarity between Chinese words. The semantic Dictionary-based method includes dictionaries such as HowNet, DaCilin, Tongyici Cilin (Extended) and Antonym. The experiments are performed on 500 pairs of words and the Spearman correlation coefficient of test data is 0.524, which shows that the proposed method is feasible and effective.
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Acknowledgements
This work is supported by the National 863 Project of China (2015AA015407), National Natural Science Foundation of China (61373082, 61673248, 61502287), Shanxi Platform Project (2014091004-0103), Scholarship Council (2013-015), Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China (CAAC-ISECCA-201402) and Shanxi Higher School Science and Technology Innovation Project (2015104, 201505).
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Guo, S., Guan, Y., Li, R., Zhang, Q. (2016). Chinese Word Similarity Computing Based on Combination Strategy. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_67
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DOI: https://doi.org/10.1007/978-3-319-50496-4_67
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