Learning Entailment Rules for Unary Templates

Most work on unsupervised entailment rule acquisition focused on rules between templates with two variables, ignoring unary rules entailment rules between templates with a single variable. In this paper we investigate two approaches for unsupervised learning of such rules and compare the proposed methods with a binary rule learning method. The results show that the learned unary rule-sets outperform the binary rule-set. In addition, a novel directional similarity measure for learning entailment, termed Balanced-Inclusion, is the best performing measure.