Language Evolution and Computation Bibliography

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.

Almost all language networks in word and syntactic levels are small-world and scale-free. This raises the questions of whether a language network in deeper semantic or cognitive level also has the similar properties. To answer the question, we built up a Chinese semantic network based on a treebank with semantic role (argument structure) annotation and investigated its global statistical properties. The results show that although semantic network is also small-world and scale-free, it is different from syntactic network in hierarchical structure and K-Nearest-Neighbor correlation.