Large-Scale Acquisition of Entailment Pattern Pairs by Exploiting Transitivity

Julien Kloetzer, Kentaro Torisawa, Chikara Hashimoto, Jong-Hoon Oh

Abstract: We propose a novel method for acquiring entailment pairs of binary patterns on a large-scale. This method exploits the transitivity of entailment and a self-training scheme to improve the performance of an already strong supervised classifier for entailment, and unlike previous methods that exploit transitivity, it works on a largescale. With it we acquired 138.1 million pattern pairs with 70% precision with such non-trivial lexical substitution as “use Y to distribute X”!“X is available on Y” whose extraction is considered difficult. This represents 50.4 million more pattern pairs (a 57.5% increase) than what our supervised baseline extracted at the same precision.