RI: Small: Adapting a Natural Logic Reasoning Platform to the Task of Entailment Inference

University Of Rochester, Rochester NY

Investigators

Abstract

Current AI systems still lack the knowledge and reasoning abilities needed to handle the semantic subtleties of language and the thematic breadth of human discourse and thinking. This project is developing a basic repertoire of lexical and other general knowledge for use in a powerful inference engine (EPILOG) designed expressly to support unrestricted language understanding and reasoning. The methods being employed exploit the insights into language-based inference gained in recent years in the area of "natural Logic", which makes systematic use of word-level and structural entailment properties of language. These are easily modeled in EPILOG, which uses a language-like meaning representation (Episodic Logic). Some very general semantic properties are being manually encoded, and in addition, large numbers of knowledge items are being extracted computationally from lexical resources such as WordNet and VerbNet, and from word similarity or paraphrase clusters derived from large text corpora. The expected result is a knowledge base of fundamental lexical and other commonsense knowledge that will allow demonstration of many previously infeasible language-based inferences, including both forward and backward reasoning and many multi-premise entailment inferences in existing test suites. This will significantly advance the state of the art in basic language understanding and in mechanizing "obvious inferences", with potential applications to intelligent dialogue-based agents (for question answering, tutoring, personal assistance, etc.), and to knowledge bootstrapping through machine reading. The results will be disseminated both through papers at conferences and in journals, and through web sites making available EPILOG and the newly developed knowledge bases.