Learning Semantically Rich Event Inference Rules Using Definition of Verbs

Natural language understanding is a key requirement for many NLP tasks. Deep language understanding, which enables inference, requires systems that have large amounts of knowledge enabling them to connect natural language to the concepts of the world. We present a novel attempt to automatically acquire conceptual knowledge about events in the form of inference rules by reading verb definitions. We learn semantically rich inference rules which can be actively chained together in order to provide deeper understanding of conceptual events. We show that the acquired knowledge is precise and informative which can be potentially employed in different NLP tasks which require language understanding.