10. A method for constructing a statistical machine translation system that adapts translation according to context, the method comprising: obtaining a collection of bilingual document pairs in electronic format, each comprising a source language portion having a source language sequence and a target language portion having a target language sequence: identifying translations within the bilingual document pairs by word alignment or phrase alignment; and constructing at least one of: an adaptive language model that includes a language model in the target language associated with each of a plurality of clusters of the bilingual document pairs, the bilingual document pairs being clustered by similarity of translation usage; and a bilingual sublanguage space constructed by: constructing a joint count table for each bilingual document pair, or for each cluster of the bilingual document pairs that assemble document pairs that translate the aligned words or aligned phrases similarly, computing conditional probabilities from each joint count table, and defining a similarity measure to map an arbitrary source text unit to a weighted combination of the joint count tables, where joint count tables corresponding to source language of the bilingual document pairs that are more similar to the source text unit are assigned greater weights, and joint count tables corresponding to source language of the bilingual document pairs that are more dissimilar from the source text unit are assigned lower weights, whereby the statistical machine translation system is adapted to apply at least one of the adaptive language models, or the weighted combination of the tables to carry out translation of a translation unit based on similarity of the source text unit associated with the translation unit to the source document pairs, or clusters.
13. The method of claim 10 , where the adaptive language model is constructed by: training a language model for each cluster on all the target-language documents in that cluster; training a language model for each cluster on all the target-language documents in that cluster, the cluster assembling bilingual document pairs into clusters in accordance with similarity of translation usage within the bilingual document pairs; training a language model for each cluster on all the target-language documents in that cluster along with additional unilingual target-language texts; training a language model for each cluster on all the target-language documents in that cluster along with an additional of unilingual target-language texts obtained from a large collection of documents using information retrieval techniques; or providing program instructions for estimating the language model based on the target language portions of said bilingual document pairs in the clusters, in dependence on the measure of similarity between the cluster and the source text unit, thereby permitting the language model to be adapted.