Measuring linguistic complexity in Chinese: An information-theoretic approach

The measurement of linguistic complexity has been a heated topic in linguistics. While previous studies have mainly centered around English and other European languages, Chinese seems neglected to some extent, and the existing Chinese linguistic complexity metrics have room for improvement. Against these backdrops, the present study applied an information-theoretic approach – the Kolmogorov complexity system – to measure Chinese linguistic complexity and examined its reliability and validity. To this end, a corpus of c.a. 60 million characters was compiled, based on which the morphological, syntactical and overall Kolmogorov complexity metrics and 18 other complexity metrics used in previous studies were calculated and subjected to a series of correlation analyses. The results show that the three Kolmogorov complexity metrics are significantly correlated with each other and with most of the previously-used metrics at the character, lexical, syntactic and collocation tiers, indicating that the Kolmogorov complexity metrics are reliable in measuring Chinese. In terms of validity, a comparison between the Kolmogorov complexity of Chinese and nine European languages and a comparison between the Kolmogorov complexity of Chinese L1 and L2 of different proficiencies show that the Kolmogorov complexity precisely and parsimoniously captured the linguistic features of Chinese. The morphological Kolmogorov complexity reflects the richness of Chinese morphemes and the flexibility of word formation, and the syntactic Kolmogorov complexity supports the view that Chinese is a topic-prominent language. This study both extends the generalizability of Kolmogorov complexity and refines the measurement of the Chinese language.

Xun Liu, Feng Li, Wei Xiao