The field of Cross-Language Information Retrieval relates techniques close to both the Machine Translation and Information Retrieval fields, although in a context involving characteristics of its own. The present study looks to widen our knowledge about the effectiveness and applicability to that field of non-classical translation mechanisms that work at character n-gram level. For the purpose of this study, an n-gram based system of this type has been developed. This system requires only a bilingual machine-readable dictionary of n-grams, automatically generated from parallel corpora, which serves to translate queries previously n-grammed in the source language. n-Gramming is then used as an approximate string matching technique to perform monolingual text retrieval on the set of n-grammed documents in the target language. The tests for this work have been performed on CLEF collections for seven European languages, taking English as the target language. After an initial tuning phase in order to analyze the most effective way for its application, the results obtained, close to the upper baseline, not only confirm the consistency across languages of this kind of character n-gram based approaches, but also constitute a further proof of their validity and applicability, these not being tied to a given implementation. 
On the feasibility of character n-grams pseudo-translation for Cross-Language Information Retrieval tasks