Concept Models for Domain-Specific Search

  • Edgar Meij
  • Maarten de Rijke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5706)


We describe our participation in the 2008 CLEF Domain-specific track. We evaluate blind relevance feedback models and concept models on the CLEF domain-specific test collection. Applying relevance modeling techniques is found to have a positive effect on the 2008 topic set, in terms of mean average precision and precision@10. Applying concept models for blind relevance feedback, results in even bigger improvements over a query-likelihood baseline, in terms of mean average precision and early precision.


Language modeling Blind relevance feedback Concept models 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anick, P.: Using terminological feedback for web search refinement: a log-based study. In: SIGIR 2003 (2003)Google Scholar
  2. 2.
    Balog, K., Weerkamp, W., de Rijke, M.: A few examples go a long way: constructing query models from elaborate query formulations. In: SIGIR 2008 (2008)Google Scholar
  3. 3.
    Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: ACL 1996 (1996)Google Scholar
  4. 4.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistical Society. Series B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Hiemstra, D.: A linguistically motivated probabilistic model of information retrieval. In: Nikolaou, C., Stephanidis, C. (eds.) ECDL 1998. LNCS, vol. 1513, p. 569. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Lafferty, J., Zhai, C.: Document language models, query models, and risk minimization for information retrieval. In: SIGIR 2001 (2001)Google Scholar
  7. 7.
    Lavrenko, V., Croft, B.W.: Relevance based language models. In: SIGIR 2001 (2001)Google Scholar
  8. 8.
    Meij, E., de Rijke, M.: Thesaurus-based feedback to support mixed search and browsing environments. In: Kovács, L., Fuhr, N., Meghini, C. (eds.) ECDL 2007. LNCS, vol. 4675, pp. 247–258. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Meij, E., Trieschnigg, D., de Rijke, M., Kraaij, W.: Parsimonious concept modeling. In: SIGIR 2008 (2008)Google Scholar
  10. 10.
    Meij, E., Weerkamp, W., Balog, K., de Rijke, M.: Parsimonious relevance models. In: SIGIR 2008 (2008)Google Scholar
  11. 11.
    Mitra, M., Singhal, A., Buckley, C.: Improving automatic query expansion. In: SIGIR 1998 (1998)Google Scholar
  12. 12.
    Ponte, J.M., Croft, W.B.: A language modeling approach to information retrieval. In: SIGIR 1998 (1998)Google Scholar
  13. 13.
    Rocchio, J.: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice Hall, Englewood Cliffs (1971)Google Scholar
  14. 14.
    Trieschnigg, D., Meij, E., de Rijke, M., Kraaij, W.: Measuring concept relatedness using language models. In: SIGIR 2008 (2008)Google Scholar
  15. 15.
    Xu, J., Croft, W.B.: Query expansion using local and global document analysis. In: SIGIR 1996 (1996)Google Scholar
  16. 16.
    Zhai, C.: Risk Minimization and Language Modeling in Text Retrieval. PhD thesis, Carnegie Mellon University (2002)Google Scholar
  17. 17.
    Zhai, C., Lafferty, J.: Model-based feedback in the language modeling approach to information retrieval. In: CIKM 2001 (2001)Google Scholar
  18. 18.
    Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. ACM Transactions on Information Systems 22(2), 179–214 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Edgar Meij
    • 1
  • Maarten de Rijke
    • 1
  1. 1.ISLAUniversity of AmsterdamAmsterdamThe Netherlands

Personalised recommendations