Linguistics and Languages

Measuring linguistic complexity in Chinese: An information-theoretic approach

X. Liu, F. Li, et al.

Explore the groundbreaking study by Xun Liu, Feng Li, and Wei Xiao, which applies an information-theoretic approach using Kolmogorov complexity to analyze Chinese linguistic complexity. With a corpus of 60 million characters, their research reveals significant correlations and insights into morpheme richness and topic prominence. Discover how this approach compares to nine European languages and sheds light on the proficiency of Chinese L1/L2 speakers!

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Abstract

This study applies an information-theoretic approach, Kolmogorov complexity, to measure Chinese linguistic complexity. A corpus of approximately 60 million characters was used to calculate morphological, syntactical, and overall Kolmogorov complexity metrics, along with 18 other existing metrics. Results show significant correlations between the Kolmogorov metrics and the existing metrics, indicating reliability. Comparisons with nine European languages and Chinese L1/L2 speakers of varying proficiencies demonstrate the validity of the Kolmogorov approach in capturing key linguistic features of Chinese, such as morpheme richness and topic prominence.

Publisher

Humanities and Social Sciences Communications

Published On

Jul 30, 2024

Authors

Xun Liu, Feng Li, Wei Xiao

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