ISP: Learning Inferential Selectional Preferences

Abstract

Semantic inference is a key component for advanced natural language understanding. However, existing collections of automatically acquired inference rules have shown disappointing results when used in applications such as textual entailment and question answering. This paper presents ISP, a collection of methods for automatically learning admissible argument values to which an inference rule can be applied, which we call inferential selectional preferences, and methods for filtering out incorrect inferences. We evaluate ISP and present empirical evidence of its effectiveness. 1