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An Approach for Textual Entailment Recognition Based on Stacking and Voting

  • Zornitsa Kozareva
  • Andrés Montoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

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.

Keywords

Majority Vote Machine Translation Information Extraction Question Answering Word Sense Disambiguation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zornitsa Kozareva
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Departamento de Lenguajes y Sistemas InformáticosUniversidad de AlicanteSpain

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