Understanding typologies using Property Analysis: learnability, diachronic change, and formal structure

随着时间的推移，语言通过积累微小的语法差异而变化，儿童语言学习者必须对这些微小的差异敏感。一个强有力的语言变化理论必须能够阐明历史上可能的语言变化，以及这些变化是如何在代际间传播的。这个项目将通过使语言之间的距离概念具体化来解决这些问题，并将确定学习者如何在学习中利用这些微小的区别。Merchant博士提出，语法随着时间的推移而变化，学习者应对这些变化的方式与RNA（核糖核酸）序列随着时间的推移而变化的方式以及这些RNA序列产生的相应表型有相似之处。表型之间距离的遗传学概念直接适用于语言之间距离的概念，这些距离与解释语言变化直接相关。除了更好地了解语言如何变化以及学习者如何获得这些变化之外，该项目还将创建一个大型的分析语言库，使研究人员能够在该项目中获得的见解基础上进行构建。此外，该项目将为来自代表性不足群体的本科生提供有价值的STEM培训，他们将帮助开发在线数据库。该项目的技术核心是通过结合遗传学、拓扑学、认知科学和语言学的观点，发展和扩展语言、语言学习和语言变化的形式理论。特别是，该项目将通过发展和扩大三个相互关联的组成部分来完成这些任务：(1)将开发软件工具，使语言学家能够计算和比较语法的形式理论，确定语言之间的距离和邻接关系，包括语法遗传结构产生的距离和语法属性产生的距离（在Alber &amp； Prince 2017的意义上），并评估依赖不同语法距离概念的学习算法的结果。(2)创建历时相关分析语言系统的在线资源库。(3)使用语法接近性思想的学习算法将应用于存储库的分析系统，以评估历时现实和信息丰富的学习算法。这项工作将产生新的语言学习模式，历时变化，并将贡献一个重要的公共数据体。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Languages change over time by accumulating small grammatical differences, and child language learners must be sensitive to these small differences. A robust theory of language change must then be able to articulate what are possible historical language changes and how these changes are transmitted intergenerationally. This project will address these issues by making concrete the notion of distance between languages and will determine how learners can utilize these small distinctions in learning. Dr. Merchant offers that there are parallels in the way that grammars change over time and learners navigate these changes to the way that RNA (ribonucleic acid) sequences change over time and the corresponding phenotypes produced by these RNA sequences. Genetic notions of distance between phenotypes are directly applicable to notions of distances between languages, and these distances are directly relevant for explaining language change. In addition to providing a better understanding of how languages change and how learners acquire these changes, the project will create a large repository of analyzed languages allowing researchers to build on the insights gained in this project. Furthermore, the project will provide valuable STEM training for undergraduate students from underrepresented groups who will help to develop the online database. The technical core of the project involves developing and extending formal theories of language, language learning, and language change by incorporating ideas from genetics, topology, cognitive science, and linguistics. In particular, the project will accomplish these tasks by developing and expanding three interrelated components: (1) software tools will be developed that will allow linguists to calculate and compare formal theories of grammars, determine distances and adjacencies between languages, including distances arising from the genetic structure of grammars and those arising from the properties of the grammars (in the sense of Alber & Prince 2017), and evaluate the outcomes of learning algorithms that rely on different notions of grammatical distance. (2) An online repository of diachronically relevant analyzed linguistic systems will be created. (3) The learning algorithms using ideas of grammatical nearness will be applied to the analyzed systems of the repository to evaluate diachronically realistic and informative learning algorithms. This work will produce new models of language learning, diachronic change, and will contribute a significant body of publicly available data.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.