A compact forest for scalable inference over entailment and paraphrase rules

  • Authors:
  • Roy Bar-Haim;Jonathan Berant;Ido Dagan

  • Affiliations:
  • Bar-Ilan University, Ramat Gan, Israel;Tel-Aviv University, Tel-Aviv, Israel;Bar-Ilan University, Ramat Gan, Israel

  • Venue:
  • EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
  • Year:
  • 2009

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Abstract

A large body of recent research has been investigating the acquisition and application of applied inference knowledge. Such knowledge may be typically captured as entailment rules, applied over syntactic representations. Efficient inference with such knowledge then becomes a fundamental problem. Starting out from a formalism for entailment-rule application we present a novel packed data-structure and a corresponding algorithm for its scalable implementation. We proved the validity of the new algorithm and established its efficiency analytically and empirically.