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Using Lexical Expansion to Learn Inference Rules from Sparse Data

Oren Melamud, Ido Dagan, Jacob Goldberger and Idan Szpektor

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013


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 reliability 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.

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