首页> 外文期刊>The Journal of Comparative Germanic Linguistics >How variational acquisition drives syntactic change
【24h】

How variational acquisition drives syntactic change

机译:变异习得如何驱动句法变化

获取原文
获取原文并翻译 | 示例
       

摘要

Although language acquisition is frequently invoked as a cause of syntactic change, there has been relatively little work applying a formal model of acquisition to an actual case of language change and testing its predictions empirically. Here we test the model of Yang (Language Variation and Change 12: 231–250, 2000) on the historical case of the loss of verb movement to Tense (V-to-T) in Faroese and Mainland Scandinavian, using quantitative data from a number of corpora. We show that the model straightforwardly predicts the historical data, given minimal and uncontroversial assumptions about Scandinavian syntax. In contrast to a number of previous attempts to explain this repeated pattern of change, it is not necessary to appeal to any bias against learning structures involving V-to-T—a welcome result, given current evidence from acquisition. The newer V-in-situ parameter setting overcomes the original V-to-T grammar because it is more learnable in a language that also has embedded verb-second (EV2). Finally, we argue that the course of the diachronic change is evidence against a strong version of the “Rich Agreement Hypothesis” (RAH), but that under this account the stability of V-to-T in Icelandic provides evidence for the weaker version (cf. Bobaljik, Journal of Comparative Germanic Linguistics 6: 129–167, 2002).
机译:尽管经常将语言习得作为句法改变的原因,但将语言习得的正式模型应用于语言改变的实际案例并通过经验检验其预测的工作相对较少。在这里,我们使用来自法罗语和斯堪的纳维亚语的动词向时态(V-to-T)失去动词的历史案例,检验了Yang(Language Variation and Change 12:231–250,2000)的模型。语料库数。我们表明,在有关斯堪的纳维亚语法的最小和无争议的假设下,该模型可以直接预测历史数据。与先前解释这种重复变化模式的许多尝试相反,没有必要诉诸对涉及V-to-T的学习结构的偏见,这是一个令人欢迎的结果,考虑到从收购中获得的最新证据。较新的V-in-situ参数设置克服了原始的V-to-T语法,因为它在还嵌入了verb-second(EV2)的语言中更加易学。最后,我们认为历时性变化的过程是反对“丰富协议假设”(RAH)的强形式的证据,但是在此原因下,冰岛语中V-to-T的稳定性为弱版本提供了证据(参见Bobaljik,《比较日耳曼语言学杂志》 6:129–167,2002年)。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号