Dependency distance, measured by the linear distance between two syntactically related words in a sentence, is generally held as an important index of memory burden and an indicator of syntactic difficulty. Since this constraint of memory is common for all human beings, there may well be a universal preference for dependency distance minimization (DDM) for the sake of reducing memory burden. This human-driven language universal is supported by big data analyses of various corpora that consistently report shorter overall dependency distance in natural languages than in artificial random languages and long-tailed distributions featuring a majority of short dependencies and a minority of long ones. Human languages, as complex systems, seem to have evolved to come up with diverse syntactic patterns under the universal pressure for dependency distance minimization. However, there always exist a small number of long-distance dependencies in natural languages, which may reflect some other biological or functional constraints. Language system may adapt itself to these sporadic long-distance dependencies. It is these universal constraints that have shaped such a rich diversity of syntactic patterns in human languages.

依存距离通常由句子中两个句法相关单词之间的线性距离来衡量，通常被视为记忆负担的重要指标和句法难度的指标。由于这种内存约束对于所有人类都是通用的，因此为了减少内存负担，可能会普遍倾向于最小化依赖距离（DDM）。这种人类驱动的语言通用性得到各种语料库的大数据分析的支持，这些分析一致地报告自然语言中的总体依存距离比人工随机语言和长尾分布的短依存关系（大多数为短依存关系而少数为长依存关系）长。人类语言，作为复杂的系统，在最小化依赖距离的普遍压力下，似乎已经发展出各种各样的句法模式。但是，自然语言中总是存在少量的长距离依赖关系，这可能反映了其他一些生物学或功能上的限制。语言系统可能会适应这些零星的长距离依赖。正是这些普遍的制约因素造就了人类语言中如此丰富的句法模式。