Predicate-Argument Structure-Based Textual Entailment Recognition System Exploiting Wide-Coverage Lexical Knowledge
Tomohide Shibata, Sadao Kurohashi · ACM Transactions on Asian Language Information Processing · 2012
This article proposes a predicate-argument structure based Textual Entailment Recognition system exploiting wide-coverage lexical knowledge. Different from conventional machine learning approaches where several features obtained from linguistic analysis and resources are utilized, our proposed method regards a predicate-argument structure as a basic unit, and performs the matching/alignment between a text and hypothesis. In matching between predicate-arguments, wide-coverage relations between words/phrases such as synonym and is-a are utilized, which are automatically acquired from a dictionary, Web corpus, and Wikipedia.