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Dependency distance (DD) is a well-established measure of syntactic complexity. Previous studies largely focused on the linear dimension, mostly by mean of dependency distance (MDD). In the present study, a new quantitative indicator –mean hierarchical dependency distance (MHDD), is proposed to discuss DD-related issues. Combining MHDD and MDD, the study investigates syntactic complexity of different texts, using strictly length-controlled sentences of 12 text types from the Freiburg-Brown corpus of American English. Correlations of MHDD and MDD have been identified, and possible reasons are discussed from the mathematical and theoretical perspectives. Mathematically, one is that the numerator of MHDD overlaps with the denominator of MDD, both being (n-1) where n is the number of words in the sentence. The other is that the denominator of MHDD (maximum hierarchical layer: MAXHL) and the numerator of MDD (sum of DD: SOD), are positively correlated. We believe that it is the positive correlation of SOD and MAXHL that ensures the change of MDD and MHDD in the same direction. It is also worth noting that both MAXHL and SOD seem to be minimized at their respective data spectrum, which foreshadows the dependency distance minimization (DDM) tendency on the hierarchical dimension.

Syntactic Complexity of Different Text Types: From the Perspective of Dependency Distance Both Linearly and Hierarchically