RI: Small: General Knowledge Bootstrapping from Text

RI：小：从文本引导常识知识

• 批准号: 0916599
• 负责人: Lenhart Schubert
• 金额: $ 44.35万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916599&HistoricalAwards=false
• 关键词: RI; Small; General; Knowledge; Bootstrapping

The goal of this project is to extend methods of extracting general knowledge from texts, so as to obtain not only simple "factoids"such as "A door can be open" or "A person may respond to a question"(exemplifying the millions of outputs of the U. Rochester KNEXT system), but also general, conditional knowledge such as that "If a car crashes into a tree, the driver may be hurt or killed".Such conditional knowledge is crucial for intelligent agents that can understand language and make commonsense inferences. The approach employed in the project involves bootstrapping of two principal sorts: (1) abstraction from simple factoids, both individually and collectively; (2) use of already-derived factoids to boost the performance of a natural language parser/interpreter, enabling(a) extraction of more complex conditional facts from miscellaneous texts, and (b) direct interpretation of general conditional facts stated in English in sources such as Common Sense Open Mind or WordNet glosses. The evaluation methodology for the derived knowledge involves both direct human judgement and judgement of inferences automatically generated with the aid of the extracted knowledge, using the EPILOG inference engine at U. Rochester.The general knowledge obtained in this work will be made available to the broader AI community, and will advance the state of the art both in natural language understanding and in knowledge-dependent commonsense reasoning (for example, in question answering). It will also provide evidence relevant to the hypothesis that language understanding is a process dependent not only on a few thousand syntactic rules, but also on millions of pattern-like items of general knowledge that bias the parsing and interpretation process.

该项目的目标是扩展从文本中提取一般知识的方法，以便不仅获得简单的“事实“，如“一扇门可以打开”或“一个人可以回答一个问题”（例如美国数百万的输出）。罗切斯特KNEXT系统），而且还有一般的条件知识，如“如果一辆车撞到树上，司机可能会受伤或死亡”。这样的条件知识对于能够理解语言并进行常识推理的智能代理至关重要。该项目采用的方法包括两种主要类型的自举：（1）从简单的事实中抽象出来，包括单独的和集体的;（2）使用已经导出的拟事实来提高自然语言解析器/解释器的性能，使得能够（a）从混杂文本中提取更复杂的条件事实，以及（B）在诸如常识、开放思维或WordNet注释等来源中以英语陈述的一般条件事实的直接解释。对派生知识的评价方法既包括人的直接判断，也包括利用U.罗切斯特。这项工作中获得的一般知识将提供给更广泛的人工智能社区，并将推进自然语言理解和知识依赖的常识推理（例如，在问答）的最新技术水平。它还将提供与以下假设相关的证据：语言理解是一个过程，不仅依赖于几千条语法规则，还依赖于数百万个类似模式的常识项目，这些常识项目会对解析和解释过程产生偏见。