CAREER: The Grammar Matrix: Computational Linguistic Typology

职业：语法矩阵：计算语言类型学

• 批准号: 0644097
• 负责人: Emily Bender
• 金额: $ 47.42万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-03-15 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0644097&HistoricalAwards=false
• 关键词: CAREER; Grammar; Matrix; Computational; Linguistic

Linguists investigate the structure of natural languages by building models called grammars which associate strings of words with their semantic representations. As natural languages are quite complex in their structure, there are great benefits to building these models as computer software, both in managing the complex interactions of subparts of the model and in automating the testing of the model against large collections of natural language text. However, such computerized models are expensive and difficult to build, so most linguistic hypothesis testing takes place off-line. At the same time, there are great similarities among languages, suggesting that existing work on computerized models for a few well-studied languages can be leveraged to jump start the creation of models for other languages.With this CAREER award, Dr. Emily Bender aims to develop a grammar customization system that combines a cross-linguistic core grammar with a series of 'libraries' specifying alternate ways of realizing different linguistic subsystems that vary across languages (e.g., the expression of tense and aspect, coordination, or negation). This system will allow linguists to easily customize a small working grammar for a particular language, which they can then use as a test-bed for further linguistic investigation. The process of building the system itself will involve an unprecedented exploration of computational linguistic typology, exploring precise analyses of diverse linguistic phenomena across a sample of languages which show the boundaries of variation. The broader impacts of this project include the potential for quicker, more precise documentation of endangered languages and cheaper development of natural language technology (including machine translation, grammar checkers and computer-assisted language learning programs) in under-resourced languages. This project is cofunded by the Linguistics Program in the Behavioral & Cognitive Sciences Division of the Social, Behavioral & Economic Sciences Directorate and the Robust Intelligence Cluster in the Information & Intelligent Systems Division of the Computer & Information Science & Engineering Directorate.

语言学家通过构建称为语法的模型来研究自然语言的结构，该模型将单词字符串与其语义表示相关联。 由于自然语言的结构相当复杂，因此将这些模型构建为计算机软件有很大的好处，无论是在管理模型子部分的复杂交互方面，还是在针对大量自然语言文本集合自动测试模型方面。 然而，这种计算机化模型昂贵且难以构建，因此大多数语言假设检验都是离线进行的。 与此同时，语言之间存在很大的相似性，这表明可以利用一些经过深入研究的语言的计算机化模型的现有工作来快速启动其他语言的模型的创建。艾米丽·本德博士的目标是获得这一职业奖开发一个语法定制系统，将跨语言核心语法与一系列“库”相结合，指定实现不同语言的不同语言子系统的替代方法（例如，时态和体的表达、协调或否定）。 该系统将允许语言学家轻松地为特定语言定制一个小型工作语法，然后他们可以将其用作进一步语言研究的测试平台。 构建系统本身的过程将涉及对计算语言类型学的前所未有的探索，探索对显示变异边界的语言样本中的各种语言现象的精确分析。 该项目更广泛的影响包括更快、更精确地记录濒危语言以及以更便宜的成本开发资源贫乏语言的自然语言技术（包括机器翻译、语法检查器和计算机辅助语言学习程序）。该项目由社会、行为和经济科学局行为与认知科学部语言学项目和计算机与信息科学与工程局信息与智能系统部鲁棒智能集群共同资助。