A Confidence Model for Syntactically-Motivated Entailment Proofs
Asher Stern and Ido Dagan
In: RANLP 2011, 12-14 Sep 2011, Hissar, Bulgaria.

Abstract

This paper presents a novel method for recognizing textual entailment which de- rives the hypothesis from the text through a sequence of parse tree transforma- tions. Unlike related approaches based on tree-edit-distance, we employ trans- formations which better capture linguis- tic structures of entailment. This is achieved by (a) extending an earlier deter- ministic knowledge-based algorithm with syntactically-motivated on-the-fly trans- formations, and (b) by introducing an al- gorithm that uniformly learns costs for all types of transformations. Our evaluations and analysis support the validity of this ap- proach.

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