PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inferring Textual Entailment with a Probabistically Sound Calculus
Stefan Harmeling, Michael Hirsch, Suvrit Sra and Bernhard Schölkopf
Journal of Natural Language Engineering Volume 15, Number 4, pp. 450-477, 2009.

Abstract

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:6044
Deposited By:Stefan Harmeling
Deposited On:08 March 2010

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