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Learning Textual Entailment on a Distance Feature Space

  • Maria Teresa Pazienza
  • Marco Pennacchiotti
  • Fabio Massimo Zanzotto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3944)

Abstract

Textual Entailment recognition is a very difficult task as it is one of the fundamental problems in any semantic theory of natural language. As in many other NLP tasks, Machine Learning may offer important tools to better understand the problem. In this paper, we will investigate the usefulness of Machine Learning algorithms to address an apparently simple and well defined classification problem: the recognition of Textual Entailment. Due to its specificity, we propose an original feature space, the distance feature space, where we model the distance between the elements of the candidate entailment pairs. The method has been tested on the data of the Recognizing Textual Entailment (RTE) Challenge.

Keywords

Feature Space Semantic Similarity Graph Match Entailment Relation Common Subgraph 
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

  • Maria Teresa Pazienza
    • 1
  • Marco Pennacchiotti
    • 1
  • Fabio Massimo Zanzotto
    • 2
  1. 1.University of Roma Tor VergataRomaItaly
  2. 2.DISCoUniversity of Milano BicoccaMilanoItaly

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