Paper: ISP: Learning Inferential Selectional Preferences

ACL ID N07-1071
Title ISP: Learning Inferential Selectional Preferences
Venue Human Language Technologies
Session Main Conference
Year 2007

Semantic inference is a key component for advanced natural language under- standing. However, existing collections of automatically acquired inference rules have shown disappointing results when used in applications such as textual en- tailment and question answering. This pa- per presents ISP, a collection of methods for automatically learning admissible ar- gument 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 evi- dence of its effectiveness.