Without a 'doubt'? Unsupervised discovery of downward-entailing operators

Details Without a 'doubt'? Unsupervised discovery of downward-entailing operators Without a 'doubt'? Unsupervised discovery of downward-entailing operators

2009-06-12

arxiv.org/abs/0906.2415v1

Proceedings of NAACL HLT, pp. 137--145, 2009

Computer Science

Computation and Language

System output available at http://www.cs.cornell.edu/~cristian/Without_a_doubt_-_Data.html

Scientific paper

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.

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