Paper: A Two Level Model for Context Sensitive Inference Rules

ACL ID P13-1131
Title A Two Level Model for Context Sensitive Inference Rules
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

Automatic acquisition of inference rules for predicates has been commonly ad- dressed by computing distributional simi- larity between vectors of argument words, operating at the word space level. A re- cent line of work, which addresses context sensitivity of rules, represented contexts in a latent topic space and computed similar- ity over topic vectors. We propose a novel two-level model, which computes simi- larities between word-level vectors that are biased by topic-level context repre- sentations. Evaluations on a naturally- distributed dataset show that our model significantly outperforms prior word-level and topic-level models. We also release a first context-sensitive inference rule set.