The current paper presents a language independent methodology, which facilitates the creation of machine translation (MT) systems for various language pairs. This methodology is implemented in the PRESEMT hybrid MT system. PRESEMT has the lowest possible requirements on specialised resources and tools, given that for many languages (especially less widely used ones) only limited linguistic resources are available. In PRESEMT, the main translation process comprises two phases. The first one, Structure selection, determines the overall structure of a target language (TL) sentence, drawing on syntactic information from a small bilingual corpus. The second phase, Translation equivalent selection, relies on models extracted solely from monolingual corpora to implement translation disambiguation, determine intra-phrase word order and handle functional words. This paper proposes extracting information for disambiguation from the monolingual corpus. Experimental results indicate that such information substantially contributes in improving translation quality.