Authoritative Re-ranking of Search Results

  • Toine Bogers
  • Antal van den Bosch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3936)


We examine the use of authorship information in information retrieval for closed communities by extracting expert rankings for queries. We demonstrate that these rankings can be used to re-rank baseline search results and improve performance significantly. We also perform experiments in which we base expertise ratings only on first authors or on all except the final authors, and find that these limitations do not further improve our re-ranking method.


Information Retrieval Query Term Query Expansion Test Collection Baseline System 
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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  1. 1.
    Brants, T.: Natural Language Processing in Information Retrieval. In: Proc. of CLIN 2004, Antwerp, Belgium, pp. 1–13 (2004)Google Scholar
  2. 2.
    Campbell, C.S., Maglio, P.P., Cozzi, A., Dom, B.: Expertise Identification using Email Communications. In: Proc. of CIKM 2003, New Orleans, LA, pp. 528–531 (2003)Google Scholar
  3. 3.
    Chisholm, E., Kolga, T.G.: New Term Weighting Formulas for the Vector Space Method in Information Retrieval. Technical report ORNL/TM-13756, Computer Science and Mathematics Division, Oak Ridge National Laboratory (1999)Google Scholar
  4. 4.
    Giles, C.L., Bollacker, K., Lawrence, S.: CiteSeer: An Automatic Citation Indexing System. In: Proc. of Digital Libraries 1998, Pittsburgh, PA, pp. 89–98 (1998)Google Scholar
  5. 5.
    Lee, K.-S., Park, Y.-C., Choi, K.-S.: Re-ranking model based on document clusters. Information Processing & Management 37(1), 1–14 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    TREC. TREC Enterprise Track (2005),
  7. 7.
    Xu, J., Croft, W.B.: Query Expansion Using Local and Global Document Analysis. In: Proc. of SIGIR 1996, Zurich, Switzerland, pp. 4–11 (1996)Google Scholar
  8. 8.
    Zheng, Z., Srihari, R.: Optimally Combining Positive and Negative Features for Text Categorization. In: Proc. of the ICML Workshop for Learning from Imbalanced Datasets II, Washington, DC (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Toine Bogers
    • 1
  • Antal van den Bosch
    • 1
  1. 1.ILK / Language and Information SciencesTilburg UniversityTilburgThe Netherlands

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