Recognizing Inference in Texts with Markov Logic Networks

Abstract

Recognizing inference in texts (RITE) attracts growing attention of natural language processing (NLP) researchers in recent years. In this article, we propose a novel approach to recognize inference with probabilistic logical reasoning. Our approach is built on Markov logic networks (MLNs) framework, which is a probabilistic extension of first-order logic. We design specific semantic rules based on the surface, syntactic, and semantic representations of texts, and map these rules to logical representations. We also extract information from some knowledge bases as common sense logic rules. Then we utilize MLNs framework to make predictions with combining statistical and logical reasoning. Experiment results shows that our system can achieve better performance than state-of-the-art RITE systems.