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濒危语言的专门注释的数据集，这些语言只有少量的数据，验证了对这样的数据集工作的新计算技术。这开发了在“小语言”上工作的语言技术，同时也产生了关于感兴趣语言的语法的新的、计算驱动的分析见解。该项目的一个核心方面是培训，为美国学生提供机会学习这些计算工具，建立高质量的数据集，并在该项目产生的数据集的基础上建立技术平台。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、政府或学术界提供高级STEM计算语言学培训做好准备。通用依存关系(UD)是一个标准化的框架，用于建立世界上任何语言的带语法注释的语料库。UD框架由于易于用于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)管道来注释新的输入文本而获得了广泛的支持。它对于处理较小的语言特别有用，在这些语言中，丰富的注释可以弥补许多现代NLP技术预设的大型数据集的不足。该项目开发了五个新的濒危语言UD语料库，其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)至少30,000个标记的5个免费可用和完全注释的树库；(2)免费和开源的自然语言处理(NLP)管道模型，以自动执行每种目标语言的分词、词干、词法分析和句法分析，也可在面向公众的代码库中免费获得；(3)在大型多语言模型中识别区域语言簇并利用这些信息引导相关语言的NLP系统的新方法；以及(4)描述基于项目期间收集的语料库进行的比较定量和计算句法调查的主题卷。这一奖项是国家科学基金会和国家人文基金会为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金合作伙伴关系的一部分。该奖项反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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.