Researchers at the Institute of Applied Linguistics at Communication University of China say they have shown that dynamic semantic network of human language is also small-world and scale-free but it is different from syntactic network in hierarchical structure and node's degree correlation.
The research built Chinese semantic network with semantic role annotation and explored its global statistical properties. The method in this research can also be applied to other languages.
"Semantic networks, in particular, dynamic semantic networks based on real language usage, are useful to explore the organization of human semantic (or conceptual) knowledge and human performance in semantic or knowledge processing, helpful to develop better natural language processing system," noted principal investigator Haitao Liu, professor and director of Institute of Applied Linguistics at Communication University of China. "This research is the first paper to observe the dynamic semantic networks of human language."
The study shows that the semantic network tends to create a longer path length between two nodes and a greater diameter than syntactic networks. That makes semantic network a poorer hierarchy. There is a weaker correlation between the degree of a node and that of its neighbors in a semantic network than that in a syntactic network.
The disassortative property of a syntactic network can reflect the relation between content and functional words. As a result, the absence of functional words makes a flatter curve in semantic network. It is perhaps interesting to notice the similarity between syntactic and biological networks, which is demonstrating the biological foundations of language as claimed in biolinguistics. However, it needs much more explanations on why semantic network is less biological than syntactic network in the future.
Structurally, semantic network is more similar to conceptual network in the brain. Therefore the study is helpful for finding better statistical patterns to describe linguistic and cognitive universals from the viewpoint of complex networks.
This research was funded by the National Social Science Foundation of China and "211" Key Projects of Communication University of China.
Reference: Liu H T. Statistical properties of Chinese semantic networks. Chinese Sci Bull, 2009, 54: 2781―2785, doi: 10.1007/s11434-009-0467-x.