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CLEF 2005: Multilingual Retrieval by Combining Multiple Multilingual Ranked Lists

  • Luo Si
  • Jamie Callan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

We participated in two tasks: Multi-8 two-years-on retrieval and Multi-8 results merging. For the multi-8 two-years-on retrieval work, algorithms are proposed to combine simple multilingual ranked lists into a more accurate ranked list. Empirical study shows that the approach of combining multilingual retrieval results can substantially improve the accuracies over single multilingual ranked lists. The Multi-8 results merging task is viewed as similar to the results merging task of federated search. Query-specific and language-specific models are proposed to calculate comparable document scores for a small amount of documents and estimate logistic models by using information of these documents. The logistic models are used to estimate comparable scores for all documents and thus the documents can be sorted into a final ranked list. Experimental results demonstrate the advantage of the query-specific and language-specific models against several other alternatives.

Keywords

Average Precision Retrieval Method Query Expansion Mean Average Precision Retrieval Algorithm 
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

  • Luo Si
    • 1
  • Jamie Callan
    • 1
  1. 1.Language Technology InstituteSchool of Computer Science Carnegie Mellon UniversityPittsburghUSA

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