Word Semantic Similarity Research Based on Latent Relationships

被引：0
|
作者：
Lin, Xiaoqing [1 ]
Wang, Danling [2 ]
机构：
[1] Eastern Liaoning Univ, Dept Informat Technol, Dandong, Peoples R China
[2] Eastern Liaoning Univ, Art Dept, Dandong, Peoples R China
来源：
2013 2ND INTERNATIONAL SYMPOSIUM ON INSTRUMENTATION AND MEASUREMENT, SENSOR NETWORK AND AUTOMATION (IMSNA) | 2013年
关键词：
Word Similarity; VSM; SVD;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Word similarity plays an important role on fields of machine translation, semantic disambiguation, information retrieval and others. Singular value decomposition (SVD) is proposed to measure the Chinese words similarity so as to compensate for the data sparseness by vector space model (VSM). Firstly, the thesaurus is used to build the generation templates which represent the relationships between words. Word similarity scores are gotten by calculating the angle cosine between vectors. Experimental results that our accuracy is improved by 5% than traditional VSM.
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页码：168 / 171
页数：4
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