This paper describes the experiments of our team for CLEF 2001, which includes both official and post-submission runs. We took part in the monolingual task, for Dutch, German, and Italian. The focus of our experiments was on the effects of morphological analyses such as stemming and compound splitting on retrieval effectiveness. Confirming earlier reports on retrieval in compound splitting languages such as Dutch and German, we found improvements to be around 25% for German and as much as 69% for Dutch. For Italian, lexiconbased stemming resulted in gains of up to 25%. 1

We investigates the e#ectiveness of language-dependent approaches to document retrieval, such as stemming and decompounding, and constrast them with language-independent approaches, such as character n-gramming. In order to reap the benefits of more than one type of approach, we also consider the e#ectiveness of the combination of both types of approaches. We focus on document retrieval in nine European languages: Dutch, English, Finnish, French, German, Italian, Russian, Spanish, and Swedish. We look at four di#erent cross-lingual information retrieval tasks: monolingual, bilingual, multilingual, and domain-specific retrieval. The experimental evidence is obtained using the 2003 test suite of the cross-language evaluation forum (CLEF).

This paper describes the official runs of our team for CLEF 2002. We took part in the monolingual tasks for each of the seven non-English languages for which CLEF provides document collections (Dutch, Finnish, French, German, Italian, Spanish, and Swedish). We also conducted our first experiments for the bilingual task (English to Dutch, and English to German), and took part in the GIRT and Amaryllis tasks. Finally, we experimented with the combination of runs.

This paper describes our official runs for CLEF 2003. We took part in the monolingual task (for Dutch, Finnish, French, German, Italian, Russian, Spanish, and Swedish), and in the bilingual task (English to Russian, French to Dutch, German to Italian, Italian to Spanish). We also conducted our first experiments for the multilingual task (both multi-4 and multi-8), and took part in the GIRT task.

This paper describes the official runs of our team for CLEF 2002. We took part in the monolingual tasks for each of the seven non-English languages for which CLEF provides document collections (Dutch, Finnish, French, German, Italian, Spanish, and Swedish). We also conducted our first experiments for the bilingual task (English to Dutch, and English to German), and took part in the GIRT and Amaryllis tasks. Finally, we experimented with the combination of runs.

This paper addresses compound splitting for Dutch in the context of broadcast news transcription. Language models were created using original text versions and text versions that were decomposed using a data-driven compound splitting algorithm. Language model performances were compared in terms of outof -vocabulary rates and word error rates in a real-world broadcast news transcription task. It was concluded that compound splitting does improve ASR performance. Best results were obtained when frequent compounds were not decomposed.

In this paper, the current state-of-aairs in Dutch speechbased retrieval as addressed in a series of multimedia retrieval projects is described and possible future directions of the research in this eld are discussed in brief.