关于单词的“粘性”。一项比较语言研究:筛选网络上的英语、德语、法语和拉丁语短语

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS Journal of Quantitative Linguistics Pub Date : 2019-01-02 DOI:10.1080/09296174.2018.1451206
M. Berger
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引用次数: 0

摘要

抽象语言是智人的特征之一,它不仅是一个单词链。相反,单词以非随机的方式组合在一起形成短语。在这里,万维网搜索了三种现存和一种已灭绝语言的习语:1102个英语、1183个德语、1138个法语和1128个拉丁短语,分为三类,频率分别为高、中、低频。事实上,高频短语占所有英语短语的49.5%,但仅占法语短语的9.0%和德语短语的2.5%。英语、法语和德语中分别占34.9%、33.0%和24.9%。大多数法语和德语短语频率较低。在万维网上,拉丁短语与法语一样常见,比德语更常见,其频率分布与法语和德语相似。频率分布在所有四种语言的相似最大值周围产生了三个主要类别,具有不同的相对比例。事实证明,互联网可能有助于语言的定量比较。
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On the ‘Stickiness’ of Words. A Comparative Language Study Screening the Internet for English, German, French and Latin Phrases
Abstract Language, one of the defining attributes of Homo sapiens, not only deploys as a chain of words. Rather, words group together in a non-random way to form phrases. Here, the world-wide web was searched for idiomatic expressions in three living and one extinct language: 1102 English, 1183 German, 1138 French and 1128 Latin phrases distributed into three categories, with high, middle and low frequencies. High-frequency phrases such as in addition to and as a matter of fact constituted 49.5% of all English phrases, but only 9.0% of the French and 2.5% of the German ones. The middle-frequency category with classical idioms such as a bitter pill or carved in stone comprised 34.9% of the English, 33.0% of the French, and 24.9% of the German phrases. Most French and German phrases were of low frequency. Latin phrases were found as often as French and more often than German ones in the world-wide web, and exhibited a frequency distribution similar to those of French and German. Frequency distributions yielded three main categories around similar maxima for all four languages, with differing relative proportions. The internet may prove useful for the quantitative comparison of languages.
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来源期刊
CiteScore
2.90
自引率
7.10%
发文量
7
期刊介绍: The Journal of Quantitative Linguistics is an international forum for the publication and discussion of research on the quantitative characteristics of language and text in an exact mathematical form. This approach, which is of growing interest, opens up important and exciting theoretical perspectives, as well as solutions for a wide range of practical problems such as machine learning or statistical parsing, by introducing into linguistics the methods and models of advanced scientific disciplines such as the natural sciences, economics, and psychology.
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