分块处理能降低自然语言的句法复杂性吗?

Qian Lu, Chunshan Xu, Haitao Liu
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引用次数: 28

摘要

自然语言是一个多层次的复杂自适应系统。层次结构可能与语言的复杂性有很大关系。依赖距离被用来解释与句法复杂性有关的各种语言模式。然而,语言的结构特性如何使依赖距离最小化却鲜有人关注。本文计算模拟了几种分块的人工语言,并通过与普通话的比较表明,分块可以显著降低线性序列的平均依赖距离。这些结果表明,语言可能已经进化出动态分块机制,以减少复杂性,以实现有效的交流。©2016 Wiley期刊公司复杂性21:33-41,2016
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Can chunking reduce syntactic complexity of natural languages?
Natural language is a complex adaptive system with multiple levels. The hierarchical structure may have much to do with the complexity of language. Dependency Distance has been invoked to explain various linguistic patterns regarding syntactic complexity. However, little attention has been paid to how the structural properties of language to minimize dependency distance. This article computationally simulates several chunked artificial languages, and shows, through comparison with Mandarin Chinese, that chunking may significantly reduce mean dependency distance of linear sequences. These results suggest that language may have evolved the mechanism of dynamic chunking to reduce the complexity for the sake of efficient communication. © 2016 Wiley Periodicals, Inc. Complexity 21: 33–41, 2016
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CiteScore
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