Learning canonical forms of entailment rules

We propose a modular approach to paraphrase and entailment-rule learning that addresses the morphosyntactic variability of lexical-syntactic templates. Using an entailment module that captures generic morpho-syntactic regularities, we transform every identified template into a canonical form. This way, statistics from different template variations are accumulated for a single template form. Additionally, morpho-syntactic redundant rules are not acquired. This scheme also yields more informative evaluation for the acquisition quality, since the bias towards rules with many frequent variations is avoided.