RI: Small: Learning Meaning and Grammar from Interaction, Context, and the World

RI：小：从互动、情境和世界中学习意义和语法

• 批准号: 1216875
• 负责人: Daniel Jurafsky
• 金额: $ 15万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216875&HistoricalAwards=false
• 关键词: RI; Small; Learning; Meaning; Grammar

Natural language processing tasks like question answering or machine translation require sophisticated parsers: systems that extract grammatical dependency relations between words. But traditional supervised methods of training parsers rely on very expensive hand-labeled datasets, and generalize poorly to new words, grammar, languages, or genres of text. This project is pursuing three directions to significantly augment current unsupervised models of grammar induction. First is a new mathematical model of dependency parsing that draws on linguistic intuitions of constituency. Second is an architecture that jointly learns grammar and parts-of-speech, eliminating the need for supervised part-of-speech tags and hand-labeled datasets, and making grammar induction possible on a vast number of languages and genres. Third are ways to exploit new sources of data for unsupervised learning, including anchor text in web data, vastly expanding the scope of the problem from the small clean annotated treebanks commonly used in current work.Language understanding by machine is a crucial tool for our nation: machine translation makes international web sites broadly accessible, sentiment analysis helps newspapers make politics more transparent, question answering systems help people disseminate knowledge, and information extraction helps corporations and people draw insights from vast databases of documents. By improving the fundammental parsing technology that underlies each of these tasks, and making it possible to parse new languages and genres that have not been parsable before, this project has the power to vastly increase both the power and scope of these key applications.

像问答或机器翻译这样的自然语言处理任务需要复杂的解析器：提取单词之间语法依赖关系的系统。 但是，传统的有监督的语法分析器训练方法依赖于非常昂贵的手工标记数据集，并且难以推广到新词、语法、语言或文本类型。 该项目正在追求三个方向，以显着增强当前的无监督语法归纳模型。 首先是一个新的数学模型的依赖分析，借鉴语言直觉的选区。 第二个是联合学习语法和词性的架构，消除了对监督词性标签和手工标记数据集的需要，并使语法归纳在大量语言和体裁上成为可能。第三是如何利用新的数据源进行无监督学习，包括网络数据中的锚文本，大大扩展了当前工作中常用的小型干净注释树库的问题范围。机器语言理解是我们国家的一个重要工具：机器翻译使国际网站更容易访问，情感分析帮助报纸使政治更加透明，问答系统帮助人们传播知识，信息提取帮助公司和个人从庞大的文档数据库中获得见解。 通过改进这些任务的基础解析技术，并使其能够解析以前无法解析的新语言和流派，该项目有能力大大增加这些关键应用程序的功能和范围。