Inferring Textual Entailment with a Probabistically Sound Calculus
Stefan Harmeling, Michael Hirsch, Suvrit Sra and Bernhard Schölkopf
Journal of Natural Language Engineering
We introduce a system for textual entailment that is based on a probabilistic model of entailment. The model is deﬁned using a calculus of transformations on dependency trees, which is characterized by the fact that derivations in that calculus preserve the truth only with a certain probability. The calculus is successfully evaluated on the datasets of the PASCAL Challenge on Recognizing Textual Entailment.