REDUCING THE SPECTRUM OF TRANSLATION MODELS IN SUPRACORPORA DATABASES
The paper describes an approach aimed at reducing the spectrum of translation models that are registered in supracorpora databases (SDB). This approach may be applied to both professional (made by the human) and machine translation. As of today, one can use the information resources of the SDB to research the problems of interest to computing, computer linguistics and linguistic theory. Here, the focus is on examining if and how the SDB can be used in translation practice - for reducing the spectrum of translation models. Very often while translating, a translator finds himself/herself in a multiple-choice situation: due to the synonymic potential, characteristic of natural languages, in translation instead of the only possible solution, there is a set of relatively interchangeable equivalents, i.e., “fan of alternatives.” Choosing from a (sometimes wide) range of variants, a translator, in order to narrow the choice, relies on some specific characteristics of the source text. Hence, the goal of the paper is to describe the approach that would allow one to use the SDB for narrowing the choice set of relevant translation models.