Yadav Himanshu, Mittal Shubham, Husain Samar
Department of Linguistics, University of Potsdam, Germany.
Department of Chemical Engineering, Indian Institute of Technology Delhi, India.
Open Mind (Camb). 2022 Sep 15;6:147-168. doi: 10.1162/opmi_a_00060. eCollection 2022.
Dependency length minimization is widely regarded as a cross-linguistic universal reflecting syntactic complexity in natural languages. A typical way to operationalize dependency length in corpus-based studies has been to count the number of words between syntactically related words. However, such a formulation ignores the of the linguistic material that intervenes a dependency. In this work, we investigate if the number of syntactic heads (rather than the number of words) that intervene a dependency better captures the syntactic complexity across languages. We demonstrate that the dependency length minimization constraint in terms of the number of words could arise as a consequence of constraints on the intervening heads and the tree properties such as node arity. The current study highlights the importance of syntactic heads as central regions of structure building during processing. The results show that when syntactically related words are nonadjacent, increased structure building in the intervening region is avoided.
依存长度最小化被广泛认为是一种跨语言的普遍现象,反映了自然语言中的句法复杂性。在基于语料库的研究中,一种典型的操作依存长度的方法是计算句法相关词之间的单词数量。然而,这样的表述忽略了介入依存关系的语言材料的 。在这项工作中,我们研究介入依存关系的句法中心语数量(而非单词数量)是否能更好地捕捉跨语言的句法复杂性。我们证明,基于单词数量的依存长度最小化约束可能是由于对介入中心语的约束以及诸如节点元数等树形属性导致的。当前的研究强调了句法中心语在加工过程中作为结构构建核心区域的重要性。结果表明,当句法相关词不相邻时,会避免在介入区域增加结构构建。