Lexical Entailment for Information Retrieval

  • Stéphane Clinchant
  • Cyril Goutte
  • Eric Gaussier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


Textual Entailment has recently been proposed as an application independent task of recognising whether the meaning of one text may be inferred from another. This is potentially a key task in many NLP applications. In this contribution, we investigate the use of various lexical entailment models in Information Retrieval, using the language modelling framework. We show that lexical entailment potentially provides a significant boost in performance, similar to pseudo-relevance feedback, but at a lower computational cost. In addition, we show that the performance is relatively stable with respect to the corpus the lexical entailment measure is estimated on.


Information Retrieval Information Gain Query Term Mean Average Precision Jaccard Similarity 
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

  • Stéphane Clinchant
    • 1
  • Cyril Goutte
    • 1
  • Eric Gaussier
    • 1
  1. 1.Xerox Research Centre EuropeMeylanFrance

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