Model fusion experiments for the CLSR task at CLEF 2007

Abstract

This paper presents the participation of the University of Ottawa group in the Cross-Language Speech Retrieval (CL-SR) task at CLEF 2007. We present the results of the submitted runs for the English collection. We have used two Information Retrieval systems in our experiments: SMART and Terrier, with two query expansion techniques: one based on a thesaurus and the second one based on blind relevant feedback. We proposed two novel data fusion methods for merging the results of several models (retrieval schemes available in SMART and Terrier). Our experiments showed that the combination of query expansion methods and data fusion methods helps to improve the retrieval performance. We also present cross-language experiments, where the queries are automatically translated by combining the results of several online machine translation tools. Experiments on indexing the manual summaries and keywords gave the best retrieval results. © 2008 Springer-Verlag Berlin Heidelberg.