An approach for textual entailment recognition based on stacking and voting

This paper presents a machine-learning approach for the recognition of textual entailment. For our approach we model lexical and semantic features. We study the effect of stacking and voting joint classifier combination techniques which boost the final performance of the system. In an exhaustive experimental evaluation, the performance of the developed approach is measured. The obtained results demonstrate that an ensemble of classifiers achieves higher accuracy than an individual classifier and comparable results to already existing textual entailment systems.