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.

@InProceedings{Danescu-Niculescu-Mizil+Lee+Ducott:09a,

author={Cristian Danescu-Niculescu-Mizil and Lillian Lee and  Richard Ducott},

title={Without a `doubt'?  {Unsupervised} discovery of downward-entailing operators},

booktitle={Proceedings of NAACL HLT},

year={2009},

pages={137--145}

}