PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A compact forest for scalable inference over entailment and paraphrase rules
Roy Bar Haim, Jonathan Berant and Ido Dagan
In: EMNLP 2009, 6-7 August 2009, Singapore.


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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Theory & Algorithms
ID Code:6130
Deposited By:Roy Bar Haim
Deposited On:08 March 2010