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革命。然而，这场革命依赖于需要大量数据的技术。该项目构建了一系列濒危语言的特殊注释数据集，这些语言只有少量数据存在，验证了在这些数据集上工作的新计算技术。这开发了适用于“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STEM计算语言学培训。通用语料库（UD）是一个标准化的框架，用于构建世界上任何语言的语法注释语料库。UD框架由于其易于使用的定量语言分析和跨语言比较，以及由于其用于训练自然语言处理（NLP）管道以注释新输入文本的实用性而获得广泛支持。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大型数据集的缺乏，这是许多现代NLP技术的先决条件。该项目开发了五个新的濒危语言语料库，这些语料库的规模足以进行深入的科学分析并建立技术平台。该项目的最终成果是：（1）五个免费提供的、完全注释的树库，至少有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、形态分析和句法分析，也可以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，以及利用这些信息引导相关语言的NLP系统，以及（4）基于项目期间收集的语料库描述比较定量和计算句法研究的主题卷。该奖项是国家科学基金会和国家人文基金会为NSF动态语言基础设施- NEH记录濒危语言计划建立的资助伙伴关系的一部分。该奖项反映了NSF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.