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Machine Learning Challenges Workshop

MLCW 2005: Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment pp 372–384Cite as

Textual Entailment Recognition Using a Linguistically–Motivated Decision Tree Classifier

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Author information

Authors and Affiliations

  1. School of Computer Science and Informatics, University College Dublin, Ireland

    Eamonn Newman, John Dunnion & Joe Carthy

  2. NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Australia

    Nicola Stokes

Authors
  1. Eamonn Newman
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  2. Nicola Stokes
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  3. John Dunnion
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  4. Joe Carthy
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Editor information

Editors and Affiliations

  1. Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tübingen, Germany

    Joaquin Quiñonero-Candela

  2. Bar Ilan University, 52900, Ramat Gan, Israel

    Ido Dagan

  3. ITC-IRST, Trento, Italy

    Bernardo Magnini

  4. Université d’Evry-Val d’Essonne, IBISC CNRS FRE 2873 and GENPOLE, 523, Place des terrasses, 91000, Evry, France

    Florence d’Alché-Buc

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© 2006 Springer-Verlag Berlin Heidelberg

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Newman, E., Stokes, N., Dunnion, J., Carthy, J. (2006). Textual Entailment Recognition Using a Linguistically–Motivated Decision Tree Classifier. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_21

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  • DOI: https://doi.org/10.1007/11736790_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33427-9

  • Online ISBN: 978-3-540-33428-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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