Paper: A Compact Forest for Scalable Inference over Entailment and Paraphrase Rules

ACL ID D09-1110
Title A Compact Forest for Scalable Inference over Entailment and Paraphrase Rules
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2009
Authors

A large body of recent research has been investigating the acquisition and applica- tion 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 funda- mental problem. Starting out from a for- malism 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 effi- ciency analytically and empirically.