Negation for Document Re-ranking in Ad-hoc Retrieval

  • Pierpaolo Basile
  • Annalina Caputo
  • Giovanni Semeraro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6931)


Information about top-ranked documents plays a key role to improve retrieval performance. One of the most common strategies which exploits this kind of information is relevance feedback. Few works have investigated the role of negative feedback on retrieval performance. This is probably due to the difficulty of dealing with the concept of non-relevant document. This paper proposes a novel approach to document re-ranking, which relies on the concept of negative feedback represented by non-relevant documents. In our model the concept of non-relevance is defined as a quantum operator in both the classical Vector Space Model and a Semantic Document Space. The latter is induced from the original document space using a distributional approach based on Random Indexing. The evaluation carried out on a standard document collection shows the effectiveness of the proposed approach and opens new perspectives to address the problem of quantifying the concept of non-relevance.


Relevance Feedback Mean Average Precision Ideal Document Context Vector Quantum Negation 
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 2011

Authors and Affiliations

  • Pierpaolo Basile
    • 1
  • Annalina Caputo
    • 1
  • Giovanni Semeraro
    • 1
  1. 1.Dept. of Computer ScienceUniversity of Bari “Aldo Moro”BariItaly

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