Using Answer Set Programming in an Inference-Based approach to Natural Language Semantics

In this paper we describe an inference-based approach to understand natural language. We present a fragment of Reiter's default logic and its use in representing knowlege about the norms of the car crashes domain. This fragment is used in a non-monotonic reasoning system to infer the cause of an accident from its description in the analyzed text. We present then a method to translate the default rules into Extended Logic Program in order to use Answer Set Programming to implement our system