Without a 'doubt'?: unsupervised discovery of downward-entailing operators

Authors:
Cristian Danescu-Niculescu-Mizil;Lillian Lee;Richard Ducott
Affiliations:
Cornell University, Ithaca, NY;Cornell University, Ithaca, NY;Cornell University, Ithaca, NY
Venue:
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2009

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Abstract

An important part of textual inference is making deductions involving monotonicity, that is, determining whether a given assertion entails restrictions or relaxations of that assertion. For instance, the statement 'We know the epidemic spread quickly' does not entail 'We know the epidemic spread quickly via fleas', but 'We doubt the epidemic spread quickly' entails 'We doubt the epidemic spread quickly via fleas'. Here, we present the first algorithm for the challenging lexical-semantics problem of learning linguistic constructions that, like 'doubt', are downward entailing (DE). Our algorithm is unsupervised, resource-lean, and effective, accurately recovering many DE operators that are missing from the hand-constructed lists that textual-inference systems currently use.