A compact forest for scalable inference over entailment and paraphrase rules

Roy Bar-Haim; Jonathan Berant; Ido Dagan

doi:10.3115/1699648.1699651

A compact forest for scalable inference over entailment and paraphrase rules

Roy Bar-Haim, Jonathan Berant, Ido Dagan

Department of Computer Science

Research output: Contribution to conference › Paper › peer-review

10 Scopus citations

Abstract

A large 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.

Original language	English
Pages	1056-1065
Number of pages	10
DOIs	https://doi.org/10.3115/1699648.1699651
State	Published - 2009
Event	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009 - Singapore, Singapore Duration: 6 Aug 2009 → 7 Aug 2009

Conference

Conference	2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009
Country/Territory	Singapore
City	Singapore
Period	6/08/09 → 7/08/09

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10.3115/1699648.1699651

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A compact forest for scalable inference over entailment and paraphrase rules. / Bar-Haim, Roy; Berant, Jonathan; Dagan, Ido.
2009. 1056-1065 Paper presented at 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, Held in Conjunction with ACL-IJCNLP 2009, Singapore, Singapore.

Research output: Contribution to conference › Paper › peer-review

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A compact forest for scalable inference over entailment and paraphrase rules

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