语言变化的动力学建模:Logistic回归、Piotrowski定律和波兰语中的一大堆例子

IF 0.7 2区 文学 0 LANGUAGE & LINGUISTICS Journal of Quantitative Linguistics Pub Date : 2021-04-13 DOI:10.1080/09296174.2022.2151208
Rafal L. Górski, Maciej Eder
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引用次数: 0

摘要

摘要本研究讨论了通过逻辑回归对历时过程进行建模。语言中的非线性变化现象最早是由Raimund Piotrowski观察到的(因此被称为Piotrowsky定律),即使实际的语言证据经常反对在这种情况下使用“定律”的概念。在我们的研究中,我们将逻辑回归模型应用于15世纪至18世纪波兰语的变化。这些变化的大部分经过证实的过程与预期值密切相关,这证明语言变化可能确实类似于非线性相变场景。我们还扩展了Piotrowski的原始方法,提出了这些情况下的多项式逻辑回归,其标准版本几乎无法描述。此外,我们建议联合考虑个别语言变化情况,以检查它们可能的共线性,或者更可能的是,它们在时间函数中的不同动态。最后但同样重要的是,我们通过测试子核心大小对模型拟合优度的影响来评估我们的结果。
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Modelling the Dynamics of Language Change: Logistic Regression, Piotrowski’s Law, and a Handful of Examples in Polish
ABSTRACT The study discusses modelling diachronic processes by logistic regression. The phenomenon of nonlinear changes in language was first observed by Raimund Piotrowski (hence labelled as Piotrowski’s law), even if actual linguistic evidence often speaks against using the notion of a ‘law’ in this context. In our study, we apply logistic regression models to changes which occurred between 15th and 18th century in the Polish language. The attested course of the majority of these changes closely follow the expected values, which proves that the language change might indeed resemble a nonlinear phase change scenario. We also extend the original Piotrowski’s approach by proposing polynomial logistic regression for these cases which can hardly be described by its standard version. Also, we propose to consider individual language change cases jointly, in order to inspect their possible collinearity or, more likely, their different dynamics in the function of time. Last but not least, we evaluate our results by testing the influence of the subcorpus size on the model’s goodness-of-fit.
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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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