Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
A stemming procedure and stopword list for general French corpora
Journal of the American Society for Information Science
The XTRIEVAL Framework at CLEF 2007: Domain-Specific Track
Advances in Multilingual and Multimodal Information Retrieval
CLEF 2008: ad hoc track overview
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Data fusion for effective european monolingual information retrieval
CLEF'04 Proceedings of the 5th conference on Cross-Language Evaluation Forum: multilingual Information Access for Text, Speech and Images
CLEF 2008: ad hoc track overview
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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This article describes post workshop experiments that were conducted after our first participation at the TEL@CLEF task. We used the Xtrieval framework [5], [4] for the preparation and execution of the experiments. We ran 69 experiments in the setting of the CLEF 2008 task, whereof 39 were monolingual and 30 were cross-lingual. We investigated the capabilities of the current version of Xtrieval, which could use the two retrieval cores Lucene and Lemur from now on. Our main goal was to compare and combine the results from those retrieval engines. The translation of the topics for the cross-lingual experiments was realized with a plug-in to access the Google AJAX language API. The performance of our monolingual experiments was better than the best experiments we submitted during the evaluation campaign. Our crosslingual experiments performed very well for all target collections and achieved between 87% and 100% of the monolingual retrieval effectiveness. The combination of the results from the Lucene and the Lemur retrieval core showed very consistent performance.