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Textual Entailment Recognition Using a Linguistically–Motivated Decision Tree Classifier

  • Eamonn Newman
  • Nicola Stokes
  • John Dunnion
  • Joe Carthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3944)

Abstract

In this paper we present a classifier for Recognising Textual Entailment (RTE) and Semantic Equivalence. We evaluate the performance of this classifier using an evaluation framework provided by the PASCAL RTE Challenge Workshop. Sentence–pairs are represented as a set of features, which are used by our decision tree classifier to determine if an entailment relationship exisits between each sentence–pair in the RTE test corpus.

Keywords

Latent Semantic Indexing Sentence Pair Longe Common Subsequence Lexical Resource Document Summarisation 
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

  • Eamonn Newman
    • 1
  • Nicola Stokes
    • 2
  • John Dunnion
    • 1
  • Joe Carthy
    • 1
  1. 1.School of Computer Science and InformaticsUniversity College DublinIreland
  2. 2.NICTA Victoria Laboratory, Department of Computer Science and Software EngineeringUniversity of MelbourneAustralia

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