Logistic Regression for Metadata: Cheshire Takes on Adhoc-TEL

In this paper we will briefly describe the approaches taken by the Berkeley Cheshire Group for the Adhoc-TEL 2008 tasks (Mono and Bilingual retrieval). Since the Adhoc-TEL task is new for this year, we took the approach of using methods that have performed fairly well in other tasks. In particular, the approach this year used probabilistic text retrieval based on logistic regression and incorporating blind relevance feedback for all of the runs. All translation for bilingual tasks was performed using the LEC Power Translator PC-based MT system. This approach seems to be a fit good for the limited TEL records, since the overall results show Cheshire runs in the top five submitted runs for all languages and tasks except for Monolingual German.

Authors and Affiliations

1. School of Information, University of California, Berkeley, USA

   Ray R. Larson

Editors and Affiliations

1. Istituto di Scienza e Tecnologie dell’Informazione, CNR, Pisa, Italy

   Carol Peters

2. RWTH Aachen University, Aachen, Germany

   Thomas Deselaers

3. University of Padua, Padua, Italy

   Nicola Ferro

4. LSI-UNED, Madrid, Spain

   Julio Gonzalo  & Anselmo Peñas  & 

5. Dublin City University, Dublin 9, Ireland

   Gareth J. F. Jones

6. Helsinki University of Technology, Espoo, Finland

   Mikko Kurimo

7. University of Hildesheim, Hildesheim, Germany

   Thomas Mandl

8. Humboldt University Berlin, Germany

   Vivien Petras

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