ABSTRACT Previous studies of dependency distance as a measure of, or a proxy for, syntactic complexity do not consider factors such as sentence length and root distance. In the present study, we propose a new algorithm, i.e. Normalized Dependency Distance (NDD), that takes sentence length and root distance into consideration. Our analysis showed that exponential distribution fit well the distribution model of NDD as it did with Mean Dependency Distance (MDD), the algorithm used in previous studies. Findings indicated that NDD is significantly less dependent on sentence length than MDD is, which suggests that the new algorithm may have, to some extent, addressed the issue of MDD’s dependency on sentence length. It is argued that NDD may serve as a measure of syntactic complexity, which is a kind of universality limited by the capacity of human working memory.

中文翻译：

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标准化的相依距离：提出新措施

摘要先前关于依存距离作为句法复杂性的度量或替代的研究并未考虑诸如句子长度和词根距离之类的因素。在本研究中，我们提出了一种新的算法，即归一化依赖距离（NDD），该算法考虑了句子长度和词根距离。我们的分析表明，指数分布与NDD的分布模型非常吻合，这与以前研究中使用的算法“平均相依距离”（MDD）一样。研究结果表明，NDD对句子长度的依赖性明显小于MDD，这表明新算法在某种程度上可以解决MDD对句子长度的依赖性问题。有人认为，NDD可以用来衡量句法复杂性，