Using lexical expansion to learn inference rules from sparse data

Automatic acquisition of inference rules for predicates is widely addressed by computing distributional similarity scores between vectors of argument words. In this scheme, prior work typically refrained from learning rules for low frequency predicates associated with very sparse argument vectors due to expected low reliability. 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 expansion significantly improves performance in comparison to state-of-The-art baselines.