Instance-based Evaluation of Entailment Rule Acquisition
Idan Szpektor, Eyal Shnarch and Ido Dagan
In: 45th Annual Meeting of the Association for Computational Linguistics, 23-30 June 2007, Prague, Czech Republic.
Obtaining large volumes of inference knowledge, such as entailment rules, has become a major factor in achieving robust semantic processing. While there has been substantial
research on learning algorithms for such knowledge, their evaluation methodology has been problematic, hindering further research. We propose a novel evaluation methodology for entailment rules which explicitly addresses their semantic properties and yields satisfactory human agreement levels. The methodology is used to compare
two state of the art learning algorithms, exposing critical issues for future progress.