For our participation in this CLEF evaluation campaign, the first objective was to propose and evaluate various indexing and search strategies for the Hungarian language in order to produce better retrieval effectiveness than language-independent approach (n-gram). Using both a new stemmer including some derivational suffixes removals, and a more aggressive automatic decompounding scheme, we were able to produce better retrieval effectiveness than corresponding 4-gram indexing scheme. Our second objective was to obtain a better picture of the relative merit of various search engines with the French, Brazilian/Portuguese and Bulgarian languages. To do so we evaluated these test-collections using the Okapi, Divergence from Randomness (DFR) and language model (LM) models together with nine vector-processing approaches. After pseudorelevance feedback, either the DFR or the LM approach tends to produce the best IR performance. For the Bulgarian language, we also found that word-based indexing proposes usually better retrieval effectiveness than corresponding 4-gram indexing. In the bilingual track, we evaluated the effectiveness of various machine translation systems to automatically translate a query submitted in English into the French and Portuguese languages.