Language clusters based on linguistic complex networks  

作　　者：LIU HaiTao LI WenWen 

机构地区：[1]School of International Studies, Zhejiang University, Hangzhou 310058, China [2]Institute of Applied Linguistics, Communication University of China, Beijing 100024, China

出　　处：《Chinese Science Bulletin》2010年第30期3458-3465,共8页

基　　金：supported by Communication University of China as one of "211" Key Projects and was supported by the National Social Science Foundation of China (09BYY024)

摘　　要：To investigate the feasibility of using complex networks in the study of linguistic typology,this paper builds and explores 15 linguistic complex networks based on the dependency syntactic treebanks of 15 languages. The results show that it is possible to classify human languages by means of the following main parameters of complex networks:(a) average degree of the node,(b) cluster coefficients,(c) average path length,(d) network centralization,(e) diameter,(f) power exponent of degree distribution,and (g) the determination coefficient of power law distributions. The precision of this method is similar to the results achieved by means of modern word order typology. This paper tries to solve two problems of current linguistic typology. First,the language sample of a typological study is not real text; second,typological studies pay too much attention to local language structures in the course of choosing typological parameters. This study performs better in global typological features of language and not only enhances typological methods,but it is also valuable for developing the applications of complex networks in the humanities,social,and life sciences.To investigate the feasibility of using complex networks in the study of linguistic typology, this paper builds and explores 15 linguistic complex networks based on the dependency syntactic treebanks of 15 languages. The results show that it is possible to classify human languages by means of the following main parameters of complex networks： （a） average degree of the node, （b） cluster coefficients, （c） average path length, （d） network centralization, （e） diameter, （f） power exponent of degree distribution, and （g） the determination coefficient of power law distributions. The precision of this method is similar to the results achieved by means of modern word order typology. This paper tries to solve two problems of current linguistic typology. First, the language sample of a typological study is not real text; second, typological studies pay too much attention to local language structures in the course of choosing typological parameters. This study performs better in global typological features of language and not only enhances typological methods, but it is also valuable for developing the applications of complex networks in the humanities, social, and life sciences.

关 键 词：人类语言 复杂网络 集群 类型学 发展中国家 路径长度 指数分布 分布系数 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TP393.08[自动化与计算机技术—计算机科学与技术]