This paper introduces growth curve modeling for the analysis of language change in corpus linguistics. In addition to describing growth curve modeling, which is a regression-based method for studying the dynamics of a set of variables measured over time, we demonstrate the technique through an analysis of the relative frequencies of words that are increasing or decreasing over time in a multi-billion word diachronic corpus of Twitter. This analysis finds that increasing words tend to follow a trajectory similar to the s-curve of language change, whereas decreasing words tend to follow a decelerated trajectory, thereby showing how growth curve modeling can be used to uncover and describe underlying patterns of language change in diachronic corpora.

本文介绍了增长曲线建模，用于分析语料库语言学中的语言变化。除了描述增长曲线建模（这是一种基于回归的方法来研究随时间变化的一组变量的动态变化）之外，我们还通过分析单词的相对频率随着时间的推移而增加或减少来展示该技术。 Twitter的数十亿个历时性语料库。该分析发现，增加的单词倾向于遵循与语言变化的s曲线相似的轨迹，而减少的单词倾向于遵循减速的轨迹，从而表明如何利用增长曲线建模来揭示和描述语言变化的潜在模式。历时语料库。