The paper describes the results of an experimental study of statistical topic models applied to the task of automatic single-word term extraction. The English part of the Europarl parallel corpus from the socio-political domain and the Russian articles taken from online banking magazines were used as target text collections. The experiments demonstrate that topic information can improve the quality of single-word term extraction regardless of the subject area and the target language.