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音系网络中的社区结构。

Community structure in the phonological network.

机构信息

Spoken Language Laboratory, Department of Psychology, University of Kansas Lawrence, KS, USA.

出版信息

Front Psychol. 2013 Aug 27;4:553. doi: 10.3389/fpsyg.2013.00553. eCollection 2013.

Abstract

Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.

摘要

社区结构是指在一个更大的网络中存在密集连接的群体,它是人类大脑、狩猎采集者的社交网络和商业组织以及万维网等多个领域的真实网络的共同特征(Porter 等人,2009 年)。使用一种称为 Louvain 优化方法的社区检测技术,从 Vitevitch(2008)描述的语音网络的巨型组件中提取了 17 个社区。对这些社区中的词汇与随机生成的社区中的词汇进行比较的其他分析揭示了一些新的发现。较大的社区往往由短、高频、低习得率的单词组成,较小的社区往往由长、低频、高习得率的单词组成。真实社区包含的不同语音段也比随机社区少,尽管真实社区中发现的语音段的出现次数比随机社区中的相同语音段要高得多。有趣的是,社区内相对较少的双音词非常频繁出现,而大量的双音词很少出现,这反映了语言中词汇整体频率的模式(Zipf,1935)。这些发现对于理解与词汇处理相关的语音网络中单词之间激活传播的动态以及理解语言习得和语言进化的机制具有重要意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b879/3753538/d9bf07716f01/fpsyg-04-00553-g0001.jpg

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