Collaborative Research: Computational Models for Studying Word Class Distinctions in Polysynthetic Languages

协作研究：研究多合成语言中词类区别的计算模型

• 批准号: 1941742
• 负责人: Smaranda Muresan
• 金额: $ 24.93万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941742&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Models; Studying

For centuries, most language scientists have agreed that all languages have some universal building blocks, and the categories of nouns and verbs are among those. All languages are expected to have nouns, from the concrete ones such as 'table' to the abstract ones such as 'strife'. All languages are equally expected to have verbs, words such as 'go', 'eat', or 'love'. However, a persistent thread of research maintains that there are languages that do not have these categories. Polysynthetic languages investigated in this project are at the top of this list. In a polysynthetic language, words are composed of many parts (morphemes) which have independent meaning but may not be able to stand alone. If polysynthetic languages indeed do not have noun-verb distinctions, that would make them highly unusual and would create new challenges to our understanding of universal principles of cognition and speech. This project explores noun-verb distinctions in polysynthetic languages by developing new methods in computational linguistics that promote and facilitate cross-linguistic comparisons. The specific questions this research addresses are: (1) are there universal word class distinctions, particularly between nouns and verbs, and if yes, at what level does such a distinction exist? (2) can we uncover universal diagnostics for noun-verb distinctions? To answer these questions, two key issues must be addressed computationally: morphological segmentation and part-of-speech tagging of lexical items in context. The researchers propose a novel computational approach to morphological segmentation based on Adaptor Grammars that is unsupervised and is able to include linguistic knowledge as inductive bias. They also develop an unsupervised cross-lingual transfer approach for part-of-speech tagging that will be applied to a range of polysynthetic languages. As a result, the project will assemble computational tools and primary linguistic data from a diverse set of polysynthetic languages. Aside from their computational and linguistic value, the project’s results will also have significant societal impact as many polysynthetic languages are spoken in areas that are key for international security, language revitalization, and health concerns. In-depth work on grammar and corpora of polysynthetic languages will serve as the basis of new pedagogical materials to be used for the teaching and revitalization of low-resource languages.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.

几个世纪以来，大多数语言科学家都认为所有语言都有一些通用的构建模块，名词和动词的类别就是其中之一。所有的语言都应该有名词，从具体的名词如“table”到抽象的名词如“strife”。所有的语言都应该有动词，比如“go”、“eat”或“love”。然而，一个持续的研究线索认为，有语言没有这些类别。在这个项目中研究的多合成语言在这个列表中名列前茅。在一种多合成语言中，单词由许多部分（语素）组成，这些部分具有独立的意义，但可能无法独立存在。如果多合成语言确实没有名动词的区别，那将使它们非常不寻常，并将对我们理解认知和言语的普遍原则提出新的挑战。本项目通过开发促进和促进跨语言比较的计算语言学新方法，探索多合成语言中的名动词区别。本研究的具体问题是：（1）是否存在普遍的词类区分，特别是在名词和动词之间，如果是的话，这种区分存在于什么水平？(2)我们能否发现名动词区别的普遍诊断？为了回答这些问题，必须解决两个关键问题：形态分割和词性标注的词汇项目的上下文计算。研究人员提出了一种新的计算方法，基于适配器语法进行形态分割，该方法是无监督的，并且能够将语言知识作为归纳偏差。他们还开发了一种用于词性标记的无监督跨语言迁移方法，该方法将应用于一系列多合成语言。因此，该项目将从一组不同的多合成语言中汇编计算工具和主要语言数据。除了其计算和语言价值外，该项目的结果还将产生重大的社会影响，因为许多多合成语言在国际安全，语言振兴和健康问题的关键领域中使用。对多合成语言的语法和语料库的深入研究将作为新教材的基础，用于低资源语言的教学和复兴。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。

项目成果

Minimally-Supervised Morphological Segmentation using Adaptor Grammars with Linguistic Priors

使用具有语言先验的适配器语法的最小监督形态分割

• DOI: 10.18653/v1/2021.findings-acl.347
• 发表时间: 2021
• 期刊: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
• 作者: Eskander, Ramy;Lowry, Cass;Khandagale, Sujay;Callejas, Francesca;Klavans, Judith;Polinsky, Maria;Muresan, Smaranda
• 通讯作者: Muresan, Smaranda

Unsupervised Stem-based Cross-lingual Part-of-Speech Tagging for Morphologically Rich Low-Resource Languages

针对形态丰富的低资源语言的无监督基于词干的跨语言词性标注

• DOI: 10.18653/v1/2022.naacl-main.298
• 发表时间: 2022
• 期刊: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
• 作者: Eskander, Ramy;Lowry, Cass;Khandagale, Sujay;Klavans, Judith;Polinsky, Maria;Muresan, Smaranda
• 通讯作者: Muresan, Smaranda