Collaborative Research: Syntactically-annotated corpora for endangered languages in areal contact

计算机理解和使用人类语言的能力正在发生一场革命。然而，这场革命依赖于需要大量数据的技术。这个项目建立了一系列只有少量数据存在的濒危语言的特别注释数据集，验证了在这些数据集上工作的新计算技术。这就开发出了适用于“小语言”的语言技术，同时也产生了关于感兴趣语言语法的新的、计算驱动的分析见解。该项目的一个核心方面是培训，为美国学生提供学习这些计算工具的机会，建立高质量的数据集，并在项目产生的数据集之上建立技术平台。总体结果是增加了美国的语言基础设施，包括人力资源，因为该项目为学生进入私营部门、政府或学术界提供了先进的计算语言学STEM培训。通用依赖关系（Universal Dependencies， UD）是一个标准化框架，用于构建世界上任何语言的语法注释语料库。由于在定量语言分析和跨语言比较方面的易用性，以及在训练自然语言处理（NLP）管道以注释新输入文本方面的实用性，UD框架获得了广泛的支持。它对于处理较小的语言特别有用，其中丰富的注释可以弥补许多现代NLP技术所假定的大型数据集的缺乏。该项目开发了5个新的濒危语言的UD语料库，这些语料库的规模足以进行深入的科学分析和构建技术平台。该项目的最终结果是：(1) 5个免费提供且完全注释的树库，包含至少30,000个标记；(2)免费和开源的自然语言处理（NLP）管道模型，用于自动执行每种目标语言的分词、词干提取、形态分析和句法解析，也可在面向公众的代码库中免费提供；(3)在大型多语言模型中识别区域语言集群的新方法；并利用这些信息来引导相关语言的NLP系统，以及(4)一个专题卷，描述基于项目期间收集的语料库的比较定量和计算语法调查。该奖项是美国国家科学基金会和美国国家人文基金会为美国国家科学基金会动态语言基础设施- NEH记录濒危语言项目建立的资助伙伴关系的一部分。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。

There is a revolution underway in the ability for computers to understand and use human language. This revolution, though, depends on techniques that require large amounts of data. This project builds specially annotated datasets of a series of endangered languages for which only small amounts of data exist, validating new computational techniques that work on datasets such as these. This develops language technology that works on "small languages", while also generating new, computationally driven analytical insights about the grammars of the languages of interest. A core aspect of the project is training, providing opportunities for US students to learn these computational tools, to build high quality datasets, and to build technological platforms on top of the datasets produced by the project. The overall result is an increase in the United States' language infrastructure, including human resources, since the project prepares students to enter the private sector, government, or academia with advanced STEM training in computational linguistics. Universal Dependencies (UD) is a standardized framework for building syntactically annotated corpora of any language in the world. The UD framework has garnered widespread support due to its ease-of-use for quantitative linguistic analyses and cross-linguistic comparisons, and due to its utility for training natural language processing (NLP) pipelines to annotate new input texts. It is especially useful for dealing with smaller languages where rich annotation can make up for lack of large datasets, which many modern NLP techniques presuppose. This project develops five new UD corpora of endangered languages of sufficient size to do deep scientific analysis as well as to build technology platforms. The final results of this project are: (1) Five freely-available and fully-annotated treebanks of at least 30,000 tokens, (2) free and open-source natural language processing (NLP) pipeline models to automatically perform word segmentation, stemming, morphological analysis, and syntactic parsing for each of the target languages, also made freely available in public-facing code repositories, (3) novel methods for identifying areal linguistic clusters in large multilingual language models, and for leveraging this information to bootstrap NLP systems for related languages, and (4) a thematic volume describing comparative quantitative and computational syntactic investigations based on the corpora collected during the project. This award is made as part of a funding partnership between the National Science Foundation and the National Endowment for the Humanities for the NSF Dynamic Language Infrastructure – NEH Documenting Endangered Languages Program.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.