Computer-assisted textual analysis in translation: Reducing the spectrum of translation models in supracorpora databases

The paper refines the approach aimed at reducing the spectrum of translation models in supracorpora databases (SDBs). Being an information resource of broad potential application, SDBs can be used to research on problems in the field of information science, computer linguistics, medicine, etc. Here, SDBs are regarded from the perspective of the corpus-based translation studies. It is shown how this automated instrument can be applied in ‘close and distant reading’ — an approach that advocates the idea of using modern information resources in literary translation. The special focus is on opportunities that SDBs could offer for reducing the spectrum of translation models. Due to the synonymic potential, characteristic of natural languages, in translation, instead of the only possible solution, one has to choose between relatively interchangeable alternatives (words, collocations, syntactic constructions, etc.). Choosing the only one output equivalent, a translator seeks to narrow the choice set. Hence, the goal of the paper is to refine the approach that would allow using SDBs for narrowing the choice set of relevant translation models.