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.
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Kozareva, Z., Montoyo, A. (2006). An Approach for Textual Entailment Recognition Based on Stacking and Voting. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_85
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DOI: https://doi.org/10.1007/11925231_85
Publisher Name: Springer, Berlin, Heidelberg
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