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.

语言学家通过建立语法模型来研究自然语言的结构，语法模型将单词串与它们的语义表示相关联。 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的自动化模型测试方面。 然而，这种计算机化的模型是昂贵的，很难建立，所以大多数语言假设检验离线进行。 与此同时，语言之间有很大的相似性，这表明可以利用现有的一些研究良好的语言的计算机模型工作来启动其他语言的模型创建。艾米丽本德博士的目标是开发一个语法定制系统，结合了交叉，具有一系列“库”的语言核心语法指定实现跨语言变化的不同语言子系统的替代方式（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的工作语法，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，探索对语言样本中各种语言现象的精确分析，这些语言样本显示了变异的边界。 该项目的更广泛影响包括更快、更精确地记录濒危语言的潜力，以及在资源不足的语言中以更低成本开发自然语言技术（包括机器翻译、语法检查和计算机辅助语言学习程序）的潜力。该项目由社会行为经济科学理事会行为认知科学部的语言学项目和计算机信息科学工程理事会信息智能系统部的鲁棒智能集群共同资助。