Intermittency and scale-free networks: a dynamical model for human language complexity
Intermittency and scale-free networks: a dynamical model for human language complexity
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DOI：
10.1016/s0960-0779(03)00432-6
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发表时间：
2004-04-01
影响因子：
7.8
通讯作者：
Palatella, L
中科院分区：
文献类型：
--
作者：
Allegrini, P;Grigolini, P;Palatella, L
在这篇文章中，我们试图借用统计力学中的先进概念来模拟人类语言复杂性的某些特征。我们使用时间序列方法，扩散熵(DE)方法，来计算意大利报纸和杂志语料库的复杂性。我们发现，反常标度指数符合一个简单的动力学模型，即复杂无标度网络上的随机游走，该模型在语言学上与索绪尔的范例有关。网络复杂性是在同一语料库上独立测量的，看看名词和动词的共现情况。这种认知复杂性与长程时间相关性的联系也用广义中心极限定理解释了著名的齐普夫定律。(C)2003爱思唯尔有限公司。保留所有权利。
In this paper we try to model certain features of human language complexity by means of advanced concepts borrowed from statistical mechanics. We use a time series approach, the diffusion entropy (DE) method, to compute the complexity of an italian corpus of newspapers and magazines. We find that the anomalous scaling index is compatible with a simple dynamical model, a random walk on a complex scale-free network, which is linguistically related to Saussurre's paradigms. The network complexity is independently measured on the same corpus, looking at the co-occurrence of nouns and verbs. This connection of cognitive complexity with long-range time correlations also provides an explanation for the famous Zipf's law in terms of the generalized central limit theorem. (C) 2003 Elsevier Ltd. All rights reserved.