Improvemetn of Recognizing Inference in Texts

Eugenia Rabin,   Reena Bemiss

Abstract


In recent years, recognizing inference in texts (RITE) attracts growing attention of natural language processing (NLP) researchers. In this article, we propose an improved method to recognize inference with probabilistic logical reasoning. This approach is built on Markov logic networks (MLNs) framework, which is a probabilistic extension of first-order logic. Moreover, specific semantic rules based on the surface, syntactic, and map these rules to logical representations are developed. Information from some knowledge bases as common sense logic rules are extracted, and the framework are improved in order to make predictions with combining statistica data.

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