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- W1489725711 abstract "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." @default.
- W1489725711 created "2016-06-24" @default.
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- W1489725711 date "2006-01-01" @default.
- W1489725711 modified "2023-09-23" @default.
- W1489725711 title "An Approach for Textual Entailment Recognition Based on Stacking and Voting" @default.
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- W1489725711 doi "https://doi.org/10.1007/11925231_85" @default.
- W1489725711 hasPublicationYear "2006" @default.
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