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语义变化的频率模式:基于语料库的语言变化中近临界动态的证据。

Frequency patterns of semantic change: corpus-based evidence of a near-critical dynamics in language change.

作者信息

Feltgen Q, Fagard B, Nadal J-P

机构信息

Laboratoire de Physique Statistique, École Normale Supérieure, PSL Research University; Université Paris Diderot, Sorbonne Paris-Cité; Sorbonne Universités, UPMC-Univ. Paris 06; CNRS, Paris, France.

Laboratoire Langues, Textes, Traitements informatique, Cognition (Lattice, CNRS, ENS & Université Paris 3, PSL & USPC), École normale supérieure, Paris, France.

出版信息

R Soc Open Sci. 2017 Nov 8;4(11):170830. doi: 10.1098/rsos.170830. eCollection 2017 Nov.

DOI:10.1098/rsos.170830
PMID:29291074
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5717648/
Abstract

It is generally believed that when a linguistic item acquires a new meaning, its overall frequency of use rises with time with an S-shaped growth curve. Yet, this claim has only been supported by a limited number of case studies. In this paper, we provide the first corpus-based large-scale confirmation of the S-curve in language change. Moreover, we uncover another generic pattern, a latency phase preceding the S-growth, during which the frequency remains close to constant. We propose a usage-based model which predicts both phases, the latency and the S-growth. The driving mechanism is a random walk in the space of frequency of use. The underlying deterministic dynamics highlights the role of a control parameter which tunes the system at the vicinity of a saddle-node bifurcation. In the neighbourhood of the critical point, the latency phase corresponds to the diffusion time over the critical region, and the S-growth to the fast convergence that follows. The durations of the two phases are computed as specific first-passage times, leading to distributions that fit well the ones extracted from our dataset. We argue that our results are not specific to the studied corpus, but apply to semantic change in general.

摘要

人们普遍认为,当一个语言项目获得新意义时,其总体使用频率会随时间呈S形增长曲线上升。然而,这一说法仅得到了有限数量的案例研究的支持。在本文中,我们首次基于语料库对语言变化中的S曲线进行了大规模验证。此外,我们还发现了另一种普遍模式,即在S形增长之前存在一个潜伏期,在此期间频率保持接近恒定。我们提出了一个基于用法的模型,该模型可以预测潜伏期和S形增长这两个阶段。驱动机制是在使用频率空间中的随机游走。潜在的确定性动力学突出了一个控制参数的作用,该参数在鞍结分岔附近对系统进行调整。在临界点附近,潜伏期对应于在临界区域的扩散时间,而S形增长对应于随后的快速收敛。这两个阶段的持续时间被计算为特定的首次通过时间,从而得到与从我们的数据集中提取的分布非常吻合的分布。我们认为我们的结果并非特定于所研究的语料库,而是普遍适用于语义变化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/aa35ccba796d/rsos170830-g8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/59e4ec81b1a3/rsos170830-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/cdddfcd7e668/rsos170830-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/33c0fe6514ee/rsos170830-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/f1cfd1702c44/rsos170830-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/7e8da216615c/rsos170830-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/a085a340d759/rsos170830-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/fd3c2cb850e5/rsos170830-g7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/aa35ccba796d/rsos170830-g8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/59e4ec81b1a3/rsos170830-g1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/cdddfcd7e668/rsos170830-g2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/33c0fe6514ee/rsos170830-g3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/f1cfd1702c44/rsos170830-g4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/7e8da216615c/rsos170830-g5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/a085a340d759/rsos170830-g6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/fd3c2cb850e5/rsos170830-g7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ac0/5717648/aa35ccba796d/rsos170830-g8.jpg

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