Quantifying Syntactic Complexity in Czech Texts: An Analysis of Mean Dependency Distance and Average Sentence Length Across Genres

Abstract

ABSTRACT This study investigates the syntactic complexity of various text-types in the Czech language by analysing the mean dependency distance (MDD), a measure that quantifies the average distance between syntactic heads and their dependents within a sentence, and average sentence length (ASL). Using data from the SYN2020 corpus, a large and balanced collection of contemporary written Czech, we calculate the MDD and ASL for different text-types. Our findings reveal distinct patterns in the MDD and ASL values across genres, suggesting that syntactic complexity varies among different types of texts. We observe a clear distinction between fiction and non-fiction genres, with fiction exhibiting lower MDD and ASL values, indicating a more compact syntactic structure. Non-fiction genres, particularly scientific literature, display higher MDD and ASL values, reflecting more complex syntactic constructions. Journalistic texts, such as newspapers and magazines, fall between fiction and non-fiction in terms of MDD and ASL values. These results demonstrate the potential of MDD and ASL as quantitative measures for characterizing and differentiating text-types based on their syntactic complexity. Furthermore, our analysis contributes to a deeper understanding of the syntactic variations across diverse genres in the Czech language.