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GeoCLEF: The CLEF 2005 Cross-Language Geographic Information Retrieval Track Overview

  • Fredric Gey
  • Ray Larson
  • Mark Sanderson
  • Hideo Joho
  • Paul Clough
  • Vivien Petras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

GeoCLEF was a new pilot track in CLEF 2005. GeoCLEF was to test and evaluate cross-language geographic information retrieval (GIR) of text. Geographic information retrieval is retrieval oriented toward the geographic specification in the description of the search topic and returns documents which satisfy this geographic information need. For GeoCLEF 2005, twenty-five search topics were defined for searching against the English and German ad-hoc document collections of CLEF. Topic languages were English, German, Portuguese and Spanish. Eleven groups submitted runs and about 25,000 documents (half English and half German) in the pooled runs were judged by the organizers. The groups used a variety of approaches, including geographic bounding boxes and external knowledge bases (geographic thesauri and ontologies and gazetteers). The results were encouraging but showed that additional work needs to be done to refine the task for GeoCLEF in 2006.

Keywords

Relevant Document Document Collection Search Topic Shark Attack Geographic Information Retrieval 
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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References

  1. 1.
    Clough, P.D., Sanderson, M.: A Proposal for Comparative Evaluation of Automatic Annotation for Geo-referenced Documents. In: Proceedings of Workshop on Geographic Information Retrieval, SIGIR (2004)Google Scholar
  2. 2.
    Clough, P.D.: Extracting Metadata for Spatially-Aware Information Retrieval on the Internet. In: Proceedings of GIR 2005 Workshop at CIKM 2005, Bremen, Germany, November 4, online (2005)Google Scholar
  3. 3.
    Cormack, G.V., Palmer, C.R., Clarke, C.L.A.: Efficient Construction of Large Test Collections. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 282–289 (1998)Google Scholar
  4. 4.
    Cunningham, H., Maynard, D., Bontcheva, K., Tablan, V.: GATE: A Framework and Graphical Development Environment for Robust NLP Tools and Applications. In: Proceedings of ACL 2002, Philadelphia (2002)Google Scholar
  5. 5.
    Kraaij, W., Smeaton, A.F., Over, P., Arlandis, J.: TRECVID 2004 - An Overview. In: TREC Video Retrieval Evaluation Online Proceedings (2004), http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.org.html
  6. 6.
    Sanderson, M., Joho, H.: Forming Test Collections with No System Pooling. In: Järvelin, K., Allan, J., Bruza, P., Sanderson, M. (eds.) Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, UK, pp. 33–40 (2004)Google Scholar
  7. 7.
    Sanderson, M., Zobel, J.: Information Retrieval System Evaluation: Effort, Sensitivity, and Reliability. In: Proceedings of the 28th ACM SIGIR conference, Brazil, pp. 162–169 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fredric Gey
    • 1
  • Ray Larson
    • 1
  • Mark Sanderson
    • 2
  • Hideo Joho
    • 2
  • Paul Clough
    • 2
  • Vivien Petras
    • 1
  1. 1.University of CaliforniaBerkeleyUSA
  2. 2.Department of Information StudiesUniversity of SheffieldSheffieldUK

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