Paper: Using Lexical Expansion to Learn Inference Rules from Sparse Data

ACL ID P13-2051
Title Using Lexical Expansion to Learn Inference Rules from Sparse Data
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

Automatic acquisition of inference rules for predicates is widely addressed by com- puting distributional similarity scores be- tween vectors of argument words. In this scheme, prior work typically refrained from learning rules for low frequency predicates associated with very sparse ar- gument vectors due to expected low reli- ability. To improve the learning of such rules in an unsupervised way, we propose to lexically expand sparse argument word vectors with semantically similar words. Our evaluation shows that lexical expan- sion significantly improves performance in comparison to state-of-the-art baselines.