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Deep vs. Shallow Semantic Analysis Applied to Textual Entailment Recognition

  • Óscar Ferrández
  • Rafael Muñoz Terol
  • Rafael Muñoz
  • Patricio Martínez-Barco
  • Manuel Palomar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4139)

Abstract

This paper covers two different methods of recognising entailment between the text/hypothesis pair by processing logic forms. These two methods are based on knowledge sources. The logic forms of both the text and the hypothesis are inferred by analysing the syntactic dependency relationships between their words. Both approaches use the WordNet lexical database as knowledge source and obtain a semantic similarity score by means of WordNet relations. The difference between them is the treatment of these relations. Whereas one method carries out a deeper analysis considering many WordNet relations, the other one is shallower and manages only a reduced number of relations. These two approaches have been evaluated using the PASCAL Second RTE Challenge data and evaluation methodology.

Keywords

Logic Form Semantic Relation Atomic Proposition Question Answering Dependency Tree 
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

  • Óscar Ferrández
    • 1
  • Rafael Muñoz Terol
    • 1
  • Rafael Muñoz
    • 1
  • Patricio Martínez-Barco
    • 1
  • Manuel Palomar
    • 1
  1. 1.Natural Language Processing and Information Systems Group, Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain

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