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This paper presents the results of our experiments in the monolingual English, Spanish and Portuguese tasks and the Bilingual Spanish → English, Spanish → Portuguese, English → Spanish and Portuguese → Spanish tasks. We used the Terrier Information Retrieval Platform to run experiments for both tasks using the Inverse Document Frequency model with Laplace after-effect and normalization 2. Experiments included topics processed automatically as well as topics processed manually. Manual processing of topics was carried out using gazetteers. For the bilingual task we developed a component based on the transfer approach in machine translation. Topics were pre-processed automatically to eliminate stopwords. Then topics in the source language were translated to the target language. Evaluation of results show that the approach worked well in general, but applying more natural language processing techniques would improve retrieval performance.