IRCS Conference Room
Department of Computer Science
Natural logic and natural language inference
I'll describe an approach to natural language inference based on a model of natural logic, which identifies valid inferences by their lexical and syntactic features, without full semantic interpretation. The model extends past work in natural logic, which has focused solely on semantic containment and monotonicity, to incorporate both semantic exclusion and implicativity. The system decomposes an inference problem into a sequence of atomic edits linking premise to hypothesis; predicts a lexical entailment relation for each edit using a statistical classifier; propagates these relations upward through a syntax tree according to semantic properties of intermediate nodes; and composes the resulting entailment relations across the edit sequence. I'll present evaluations of the implemented system on the FraCaS test suite and on the RTE challenges. As this work is now a few years old, I will also give an overview of some of the subsequent work it has inspired.