RI: Small: General Knowledge Bootstrapping from Text

University Of Rochester, Rochester NY

Investigators

Abstract

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.