Machine translation systems, whether rule-based, example-based, or statistical, all rely on dictionaries that are in essence mappings between individual words of the source and the target language. Criteria for the disambiguation of ambiguous words and for differences in word order between the two languages are not accounted for in the lexicon. Instead, these important issues are dealt with in the translation engines. Because the engines tend to be compact and (even with data-oriented approaches) do not fully reflect the complexity of the problem, this approach generally does not account for the more fine grained facets of word behavior. This leads to wrong generalizations and, as a consequence, translation quality tends to be poor. In this paper we suggest to approach this problem by using a new type of lexicon that is not based on individual words but on pairs of words. For each pair of consecutive words in the source language the lexicon lists the possible translations in the target language together with information on order and distance of the target words. The process of machine translation is then seen as a combinatorial problem: For all word pairs in a source sentence all possible translations are retrieved from the lexicon and then those translations are discarded that lead to contradictions when constructing the target sentence. This process implicitly leads to word sense disambiguation and to language specific reordering of words.

A new variant of structured contextual grammar, which generates dependency trees, is introduced. The new generative model, called dependency contextual grammar, improves both the strong and weak generative power of contextual grammars, while being a potential candidate for the mathematical description of dependency-based syntactic models.