使用秩频和类型标记统计比较凯尔特语的形态类型学

IF 0.7 2区文学 0 LANGUAGE & LINGUISTICS

Journal of Quantitative Linguistics Pub Date : 2020-04-02 DOI:10.1080/09296174.2018.1560122

Andrew Wilson, Rosie Harvey

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引用次数: 2

摘要

摘要先前的工作使用了Greenberg的综合指数来比较凯尔特人的三种语言——爱尔兰语、威尔士语和布列塔尼语——但没有比较其他三种语言，即苏格兰盖尔语、曼克斯语和康沃尔语。本文通过比较所有六种凯尔特语言，包括爱尔兰语的两个时期（现代早期和现代），扩展了这项早期工作。该分析基于210篇平行诗篇文本的随机样本（每种语言30篇）。然而，格林伯格的合成指数是有问题的，因为没有计算单词中语素的操作标准。因此，我们应用了一种新的类型学指标（B7），它仅基于词汇排名频率统计。我们还探讨了单独的类型令牌计数是否可以提供类似的信息。B7指标显示，爱尔兰语的两种变体，以及威尔士语和康沃尔语，都更倾向于合成主义，而马恩语则更倾向于分析主义。布列塔尼语和苏格兰盖尔语在这两个方向上都没有明显的趋势。使用类型标记统计数据的排名差异很大，并不能说明相同的情况。

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Using Rank-Frequency and Type-Token Statistics to Compare Morphological Typology in the Celtic Languages

ABSTRACT Previous work has used Greenberg’s synthetism index to compare three of the Celtic languages – Irish, Welsh, and Breton – but not the other three languages, namely Scottish Gaelic, Manx, and Cornish. This paper extends this earlier work by comparing all six Celtic languages, including two periods of Irish (Early Modern and Present Day). The analysis is based on a random sample of 210 parallel psalm texts (30 for each language). However, Greenberg’s synthetism index is problematic because there are no operational standards for counting morphemes within words. We therefore apply a newer typological indicator (B7), which is based solely on lexical rank-frequency statistics. We also explore whether type-token counts alone can provide similar information. The B7 indicator shows that both varieties of Irish, together with Welsh and Cornish, tend more towards synthetism, whereas Manx tends more towards analytism. Breton and Scottish Gaelic do not show a clear tendency in either direction. Rankings using type-token statistics vary considerably and do not tell the same story.

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来源期刊

Journal of Quantitative Linguistics Multiple-

CiteScore

2.90

自引率

7.10%

发文量

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.