Natural Logic and Natural Language Inference

Abstract

We propose a model of natural language inference which identifies valid inferences by their lexical and syntactic features, without full semantic interpreta- tion.We extend past work in natural logic, which has focused on semantic contain- ment and monotonicity, by incorporating both semantic exclusion and implicativity. Our model decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit; propagates these relations upward through a semantic composition tree according to properties of intermediate nodes; and joins the resulting entailment relations across the edit se- quence. A computational implementation of the model achieves 70% accuracy and 89% precision on the FraCaS test suite. Moreover, including this model as a compo- nent in an existing system yields significant performance gains on the Recognizing Textual Entailment challenge.