A machine learning approach to textual entailment recognition

FABIO MASSIMO ZANZOTTO, MARCO PENNACCHIOTTI, ALESSANDRO MOSCHITTI
2009 Natural Language Engineering  
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
more » ... ompare 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. https:/www.cambridge.org/core/terms. https://doi.
doi:10.1017/s1351324909990143 fatcat:qcovhpwzjzbitkcvoqr4ktl6sy