Chinese word similarity computing based on combination strategy - Details

author：

Guo, S. (Guo, S..) ^[1] | Guan, Y. (Guan, Y..) ^[2] | Li, R. (Li, R..) ^[3] | Zhang, Q. (Zhang, Q..) ^[4]

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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.　©　Springer　International　Publishing　AG　2016.

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[ 1 ] [Guo, S.]School of Computer and Information Technology, Shanxi University, Taiyuan, China
[ 2 ] [Guan, Y.]School of Computer and Information Technology, Shanxi University, Taiyuan, China
[ 3 ] [Li, R.]School of Computer and Information Technology, Shanxi University, Taiyuan, China
[ 4 ] [Li, R.]Key Laboratory of Ministry of Education for Computation Intelligence and Chinese Information Processing, Shanxi University, Taiyuan, China
[ 5 ] [Zhang, Q.]College of Mathematics and Computer Science, Fuzhou University, Fujian, China

Reprint 's Address：

[Li, R.]School of Computer and Information Technology, Shanxi UniversityChina

Email：

liru@sxu.edu.cn

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Source ：

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Monograph name： Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISSN： 0302-9743

Volume： 10102

Issue： Springer Verlag

Language： English

0 . 4 0 2

JCR@2005

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SCOPUS Cited Count： 4

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