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A Confidence Model for Syntactically-Motivated Entailment Proofs
2011
Recent Advances in Natural Language Processing
This paper presents a novel method for recognizing textual entailment which derives the hypothesis from the text through a sequence of parse tree transformations. Unlike related approaches based on tree-edit-distance, we employ transformations which better capture linguistic structures of entailment. This is achieved by (a) extending an earlier deterministic knowledge-based algorithm with syntactically-motivated on-the-fly transformations, and (b) by introducing an algorithm that uniformly
dblp:conf/ranlp/SternD11
fatcat:nzxb3vc2l5cvxh3dvlswhrxhpu