Doctoral Dissertation Research: Inferring the linguistic past of an indigenous language family of North America

博士论文研究：推断北美土著语系的语言历史

• 批准号: 2215488
• 负责人: Andrew Garrett
• 金额: $ 2.02万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2215488&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Inferring; linguistic

Languages evolve in similar ways to biological species. Recent linguistic work has used computational phylogenetic methods from the biological sciences to address questions about the linguistic past: Is French more closely related to Italian or to Spanish? When did the Germanic languages, such as German and English, begin to diverge from their common ancestor? The former is an example of inferring genetic relationships within a language family tree and the latter is an example of estimating the dates when genetically related groups diverged. There is still much to learn about the use of these methods in linguistics and how much confidence can be placed on the results they produce. To this end, this study employs computational phylogenetic methods to infer the genetic relationships and divergence dates of an Indigenous language family in North America and examines how the results differ from those of previous studies. This work also attempts to understand why these differences occur and what new light the results shed on the linguistic history of the language family.Data used in linguistic phylogenetic studies to address similar research questions have varied to a considerable degree. Therefore, it is often unclear which data type to use or how the data should be coded, and how these decisions affect the results. Additionally, different types of linguistic data may exhibit different evolutionary dynamics and using the same evolutionary model may not be appropriate for all kinds of data. Therefore, this study examines how different choices of (a) data type, such as lexical and grammatical data, (b) coding scheme, and (c) evolutionary model influence the results. To ensure that the data is coded accurately and consistently, this study involves philological, archival, and field research. As some languages are no longer spoken and contain ‘missing’ data, this study also employs the same methods for inferring the linguistic past to predict and fill in missing values in the data which can then be helpful for language revitalization efforts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

语言以与生物物种相似的方式发展。最近的语言工作使用了来自生物科学的计算系统发育方法来解决有关语言过去的问题：法语是与意大利语还是与西班牙语密切相关？诸如德语和英语之类的德语语言何时开始与他们的共同祖先有所不同？前者是在语言家族树中推断遗传关系的一个例子，而后者是估计遗传相关群体差异日期的一个例子。关于这些方法在语言学中的使用以及对它们产生的结果可以放置多少信心。为此，这项研究员工计算系统发育方法来推断北美土著语言家族的遗传关系和差异日期，并研究结果与先前研究的结果有何不同。这项工作还试图理解为什么这些差异会发生，以及结果对语言家族的语言史的新作用。语言系统发育研究用于解决类似研究问题的DATA在考虑方面有所不同。因此，通常不清楚要使用哪种数据类型或应如何编码数据，以及这些决策如何影响结果。此外，不同类型的语言数据可能存在不同的进化动力学，并且使用相同的进化模型可能不适合各种数据。因此，本研究研究了（a）数据类型的不同选择，例如词汇和语法数据，（b）编码方案以及（c）进化模型影响结果。为了确保数据得到准确，一致地编码，这项研究涉及语言学，档案和现场研究。由于某些语言不再使用并包含“缺失”数据，因此本研究还采用了相同的方法来推断语言的过去来预测和填充数据中的缺失值，然后这些奖项对语言振兴工作有所帮助。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。