Static Pruning of Terms in Inverted Files

  • Roi Blanco
  • Álvaro Barreiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4425)


This paper addresses the problem of identifying collection dependent stop-words in order to reduce the size of inverted files. We present four methods to automatically recognise stop-words, analyse the tradeoff between efficiency and effectiveness, and compare them with a previous pruning approach. The experiments allow us to conclude that in some situations stop-words pruning is competitive with respect to other inverted file reduction techniques.


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  1. 1.
    Bahle, D., Williams, H., Zobel, J.: Efficient phrase querying with an auxiliary index. In: Proc. of ACM SIGIR, pp. 215–221. ACM Press, New York (2002)Google Scholar
  2. 2.
    Carmel, D., et al.: Static index pruning for information retrieval systems. In: Proc. of ACM SIGIR, pp. 43–50. ACM Press, New York (2001)Google Scholar
  3. 3.
    Church, K., Gale, W.: Poisson mixtures. Natural Language Engineering 2(1), 163–190 (1995)Google Scholar
  4. 4.
    de Moura, E.S., et al.: Improving web search efficiency via a locality based static pruning method. In: Proc. of WWW, pp. 235–244 (2005)Google Scholar
  5. 5.
    Fox, C.: A stop list for general text. SIGIR Forum 24(1-2), 19–21 (1990)CrossRefGoogle Scholar
  6. 6.
    Lo, R.T.W., He, B., Ounis, I.: Automatically building a stopword list for an information retrieval system. In: Proc. of DIR’05, Utrecht, Netherlands (2005)Google Scholar
  7. 7.
    Moffat, A., Turpin, A.: Compression and Coding Algorithms. Kluwer Academic Publishers, Norwell (2002)Google Scholar
  8. 8.
    Rennie, J.D.M., Jaakkola, T.: Using term informativeness for named entity detection. In: Proc. of ACM SIGIR, pp. 353–360. ACM Press, New York (2005)Google Scholar
  9. 9.
    Robertson, S., Sparck Jones, K.: Relevance weighting of search terms. JASIS 27, 129–146 (1976)CrossRefGoogle Scholar
  10. 10.
    Robertson, S.E., Walker, S.: Okapi/Keenbow at TREC-8. In: Text REtrieval Conference, pp. 151–162 (2000)Google Scholar
  11. 11.
    Robertson, S.E., et al.: Okapi at TREC-4. In: Text REtrieval Conference, pp. 21–30 (1996)Google Scholar
  12. 12.
    Salton, G., Yang, C.S., Yu, C.T.: A theory of term importance in automatic text analysis. JASIS 26(1), 33–44 (1975)CrossRefGoogle Scholar
  13. 13.
    Turtle, H., Flood, J.: Query evaluation: Strategies and optimizations. IP&M 31(6), 831–850 (1995)Google Scholar
  14. 14.
    Witten, I.H., Moffat, A., Bell, T.C.: Managing Gigabytes: Compressing and Indexing Documents and Images. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Roi Blanco
    • 1
  • Álvaro Barreiro
    • 1
  1. 1.IRLab, Computer Science Department, University of CoruñaSpain

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