Light-Weight Entailment Checking for Computational Semantics

Inference tasks in computational semantics have mostly been tackled by means of first-order theorem proving tools. While this is an important and welcome development, it has some inherent limitations. First, generating first-order logic representations of natural language documents is hampered by the lack of efficient and sufficiently robust NLP tools. Second, the computational costs of deploying first-order logic theorem proving tools in realworld situations may be prohibitive. And third, the strict yes/no decisions delivered by such tools are not always appropriate. In this paper we report on an approach to inference in semantics that works on very minimal representations which can easily be generated for arbitrary domains. Moreover, our approach is computationally efficient, and provides graded outcomes instead of strict yes/no decisions. Our approach is fully implemented, and a preliminary evaluation of the approach is discussed in the paper.