The Grammar Does the Work: Functional vs. Lexical Dependency Length Minimization Across Universal Dependencies
arXiv:2607.01899v1 Announce Type: new Abstract: Dependency length minimization (DLM) is a well-documented processing universal, but previous studies report a single mean dependency distance (MDD) per language, obscuring variation across syntactic relation types. We analyze 122 languages in UD and SUD (version 2.17), showing that DLM operates on two distinct levels. Grammar-driven optimization targets functional dependencies (det, case, aux), which are universally short (mean 1.71, $\sigma$ = 0.33) and invariant across typologically diverse languages. Processing-driven optimization operates on
The paper is a new research publication in the field of computational linguistics, typical for the arXiv platform.
It contributes to fundamental understanding of language processing, which is distantly related to the theoretical underpinnings of some AI models, but has no immediate strategic implications.
This research refines our understanding of Dependency Length Minimization, offering new insights into how grammar and processing influence language structure, but it does not change current technological or strategic landscapes.
Refined theoretical understanding of linguistic universals.
Potential for marginal improvements in natural language processing models that rely on dependency parsing.
Very long-term, highly speculative influence on the design of new language-based AI architectures, if principles are translatable and scalable.
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