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，这些模型将单词的字符串与其语义表示相关联。 由于自然语言的结构非常复杂，因此将这些模型构建为计算机软件有很大的好处，这既可以管理模型子部分的复杂相互作用，又在自动化模型对大量自然语言文本的测试中进行自动化。 但是，这样的计算机模型很昂贵且难以构建，因此大多数语言假设测试都在离线进行。 同时，语言之间存在很大相似之处，这表明可以利用一些精心研究的语言的计算机化模型上的现有工作来开始为其他语言的创建创建模型。在此职业奖中，Emily Bender博士旨在开发语法定制系统，以开发一种将跨语言的核心语法与一系列“图表”相结合的，以跨越的语言来实现一系列的范围。 （例如，时态和方面的表达，协调或否定）。 该系统将允许语言学家轻松自定义一种特定语言的小型语法，然后他们可以用作测试床进行进一步的语言调查。 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，探讨了对一种语言样本的各种语言现象的精确分析，这些语言显示了变化的界限。 该项目的更广泛的影响包括濒临灭绝语言的更快，更精确的文档以及自然语言技术（包括机器翻译，语法检查器和计算机辅助语言学习计划）的廉价开发，这是有可能的。该项目是由语言学计划在社会，行为与经济科学局的行为与认知科学部和计算机与信息科学与工程局信息与智能系统部的强大情报集群中占据的。