Learning shallow semantic rules for textual entailment

In this paper we present a novel technique for integrating lexical-semantic knowledge in systems for learning textual entailment recognition rules: the typed anchors. These describe the semantic relations between words across an entailment pair. We integrate our approach in the cross-pair similarity model. Experimental results show that our approach increases performance of cross-pair similarity learning systems.