Algorithm Research & Explore
|
411-414,434

Method of word vector based on centralization similarity matrix

Xu Fan
Wang Peiyan
Cai Dongfeng
Human-Computer Intelligence Research Center, Shenyang Aerospace University, Shenyang 110136, China

Abstract

This paper studied the method of word vector based on matrix factorization. It found that there was a linear correlation between the quality of no dimension reduction matrix and the quality of word vector. Furthermore, it derived a method of the word vector, which based on a kind of centring similarity matrix. This method made the similarity between similar(dissimilar or weakly similar) words relatively enhanced(weakened). In the word similarity experiments of WS-353 and RW datasets, it verified the effectiveness of the proposed method. The highest quality of the word vectors among the two datasets is 0.289 6 and 0.180 1. Centralization can improve the quality of similarity matrix, moreover it can improve the quality of word vector.

Foundation Support

辽宁省自然科学基金计划重点项目(20170540705)
国家自然科学基金资助项目(61403262)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.08.0721
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 2
Section: Algorithm Research & Explore
Pages: 411-414,434
Serial Number: 1001-3695(2019)02-021-0411-04

Publish History

[2019-02-05] Printed Article

Cite This Article

徐帆, 王裴岩, 蔡东风. 基于中心化相似度矩阵的词向量方法 [J]. 计算机应用研究, 2019, 36 (2): 411-414,434. (Xu Fan, Wang Peiyan, Cai Dongfeng. Method of word vector based on centralization similarity matrix [J]. Application Research of Computers, 2019, 36 (2): 411-414,434. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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