A Machine learning approach to textual entailment recognition

Designing models for learning textual entailment recognizers from annotated examples is not an easy task, as it requires modeling the semantic relations and interactions involved between two pairs of text fragments. In this paper, we approach the problem by first introducing the class of pair feature spaces, which allow supervised machine learning algorithms to derive first-order rewrite rules from annotated examples. In particular, we propose syntactic and shallow semantic feature spaces, and compare them to standard ones. Extensive experiments demonstrate that our proposed spaces learn first-order derivations, while standard ones are not expressive enough to do so

A linguistic inspection of textual entailment

Recognition of textual entailment is not an easy task. In fact, early experimental evidences in [1] seems to demonstrate that even human judges often fail in reaching an agreement on the existence of entailment relation between two expressions. We aim to contribute to the theoretical and practical setting of textual entailment, through both a linguistic inspection of the textual entailment phenomenon and the description of a new promising approach to recognition, as implemented in the system we proposed at the RTE competition [2]. © Springer-Verlag Berlin Heidelberg 2005

F.M.: A linguistic inspection of textual entailment

Abstract. Recognition of textual entailment is not an easy task. In fact, early experimental evidences in [1] seems to demonstrate that even human judges often fail in reaching an agreement on the existence of entailment relation between two expressions. We aim to contribute to the theoretical and practical setting of textual entailment, through both a linguistic inspection of the textual entailment phenomenon and the description of a new promising approach to recognition, as implemented in the system we proposed at the RTE competition [2].