'Given large collections of parallel (i.e. translated) texts, it is well-known how to, by successively applying a sentence- and a word-alignment step, establish correspondences between words across languages. However, parallel texts are a scarce resource for most language pairs involving lesser-used languages. On the other hand, human second language acquisition seems not to require the reception of large amounts of translated texts, which indicates that there must be another way of crossing the language barrier. Apparently, the human capabilities are based on looking at comparable resources, i.e. texts or speech on related topics in different languages, which, however, are not translations of each other. Comparable (written or spoken) corpora are far more common than parallel corpora, thus offering the chance to overcome the data acquisition bottleneck. Despite its cognitive motivation, in the proposed project we will not attempt to simulate the complexities of human second language acquisition, but will show that it is possible by purely technical means to automatically extract information on word- and multiword-translations from comparable corpora. The aim is to push the boundaries of current approaches, which typically utilize correlations between co-occurrence patterns across languages, in several ways: 1) Eliminating the need for initial lexicons by using a bootstrapping approach which only requires a few seed translations. 2) Implementing a new methodology which first establishes alignments between comparable documents across languages, and then computes cross-lingual alignments between words and multiword-units. 3) Improving the quality of computed word translations by applying an interlingua approach, which, by relying on several pivot languages, allows a highly effective multi-dimensional cross-check. 4) We will show that, by looking at foreign citations, language translations can even be derived from a single monolingual text corpus.'