Beyond word frequency: bursts, lulls, and scaling in the temporal distributions of words.

Beyond word frequency: bursts, lulls, and scaling in the temporal distributions of words.
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DOI:
10.1371/journal.pone.0007678
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发表时间:
2009-11-11
期刊:
影响因子:
3.7
通讯作者:
Motter AE
Motter AE
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Altmann EG;Pierrehumbert JB;Motter AE

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ZIPF的发现,单词频率分布遵守生物学和物理过程和语言之间的相似之处,为复杂的系统观点奠定了基础。 ,提出可能对语言的类似发现的可能性。 通过在usenet讨论组和语言具有不同形式级别的不同数据库中考虑经常单词,我们在这里表明,同一单词的成功出现之间的距离分布显示出与泊松过程的爆发,并且具有伸展的特征指数(WEIBULL)缩放的范围很大程度上取决于语义类型 - 对每个单词的逻辑性的度量 - 我们开发了这种行为的通用模型。 单词的复发模式通过重复时代的伸展指数分布进行了很好的描述,这是一种经验缩放,无法从ZIPF定律中预期,因为使用词语是关于人类思想和活动的独特精确和强大的镜头,我们的发现也有含义。对于集体人类动态的其他公开表现。
Zipf's discovery that word frequency distributions obey a power law established parallels between biological and physical processes, and language, laying the groundwork for a complex systems perspective on human communication. More recent research has also identified scaling regularities in the dynamics underlying the successive occurrences of events, suggesting the possibility of similar findings for language as well. By considering frequent words in USENET discussion groups and in disparate databases where the language has different levels of formality, here we show that the distributions of distances between successive occurrences of the same word display bursty deviations from a Poisson process and are well characterized by a stretched exponential (Weibull) scaling. The extent of this deviation depends strongly on semantic type – a measure of the logicality of each word – and less strongly on frequency. We develop a generative model of this behavior that fully determines the dynamics of word usage. Recurrence patterns of words are well described by a stretched exponential distribution of recurrence times, an empirical scaling that cannot be anticipated from Zipf's law. Because the use of words provides a uniquely precise and powerful lens on human thought and activity, our findings also have implications for other overt manifestations of collective human dynamics.
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