RI: Small: General Knowledge Bootstrapping from Text

这个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“门可以打开”或“一个人可以回答问题”(以U·Rochester KNEXT系统的数百万个输出为例)，还可以获得一般的条件知识，如“如果汽车撞到树上，驾驶员可能受伤或死亡”。这些条件知识对于能够理解语言并进行常识性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)从简单的事实类中单独和共同地抽象；(2)使用已经派生的事实来提高自然语言解析器/解释器的性能，从而能够(A)从繁杂的文本中提取更复杂的条件事实，以及(B)直接解释常识开放思想或WordNet注释等来源中用英语陈述的一般条件事实。派生知识的评估方法包括直接人类判断和使用罗彻斯特大学的Epilog推理机自动生成推理的判断。这项工作获得的一般知识将提供给更广泛的人工智能社区，并将促进自然语言理解和依赖知识的常识推理(例如，在问题回答中)的最新发展。它还将提供与这一假设相关的证据，即语言理解是一个过程，不仅依赖于数千条句法规则，而且还依赖于数百万项模式式的常识，这些常识偏离了句法和解释过程。

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.