Learning Canonical Forms of Entailment Rules
Idan Szpektor and Ido Dagan
In: Recent Advances in Natural Language Processing (RANLP), 27-29 Sep 2007, Borovets, Bulgaria.

Abstract

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.

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