Berkeley at GeoCLEF: Logistic Regression and Fusion for Geographic Information Retrieval

  • Ray R. Larson
  • Fredric C. Gey
  • Vivien Petras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)


In this paper we will describe the Berkeley (groups 1 and 2 combined) submissions and approaches to the GeoCLEF task for CLEF 2005. The two Berkeley groups used different systems and approaches for GeoCLEF with some common themes. For Berkeley group 1 (Larson) the main technique used was fusion of multiple probabilistic searches against different XML components using both Logistic Regression (LR) algorithms and a version of the Okapi BM-25 algorithm. The Berkeley group 2 (Gey and Petras) employed tested CLIR methods from previous CLEF evaluations using Logistic Regression with Blind Feedback. Both groups used multiple translations of queries in for cross-language searching, and the primary geographically-based approaches taken by both involved query expansion with additional place names. The Berkeley1 group used GIR indexing techniques to georeference proper nouns in the text using a gazetteer derived from the World Gazetteer (with both English and German names for each place), and automatically expanded place names in topics for regions or countries in the queries by the names of the countries or cities in those regions or countries. The Berkeley2 group used manual expansion of queries, adding additional place names.


Machine Translation Query Expansion Query Translation Document Component Bilingual Task 
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 2006

Authors and Affiliations

  • Ray R. Larson
    • 1
  • Fredric C. Gey
    • 2
  • Vivien Petras
    • 1
  1. 1.School of Information Management and Systems 
  2. 2.UC Data Archive and Technical AssistanceUniversity of CaliforniaBerkeleyUSA

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