The main purpose of this exploratory study is to work out and describe the labour migrant's linguodidactic profile, to verify its didactic capacity of an instrument of pedagogic measurement of social, cognitive, ethnocultural, educational and other significant characteristics of migrants affecting the efficiency of the Russian language training courses. The interdisciplinary methodology of the research integrates principles and approaches of methods for teaching Russian as a foreign language, interdidactics, migration sociolology, culturology, anthropology and cognitive science. The Russian language training courses have a strong potential for linguocultural adaptation and integration of labour migrants because the language functions as a depository and translator of the hosting nation's moral norms and values. The effectiveness of the Russian language training course depends upon the strict consideration of all significant characteristics of its addressees, i.e. labour migrants. These characteristics were identified and then integrated into the linguodidactic profile which was taken as a basis for the language training course. The didactic capacity of the linguodidactic profile was proved on the example of language teaching and testing of migrants from the Republic

The author analyses genres of special discourse related to information technology (IT) in terms of difficulties experienced by translators of scientific and technical texts in understanding them. For this purpose, surveys of professional translators were conducted which made it possible to identify the most typical genres: press release, instruction, corporate website and user's manual. A discourse analysis was carried out of these genres according to specified criteria in order to compare and confirm the results of the survey as well as identify the easiest and the most difficult IT genres for translators to understand.

The paper describes the key concepts of a word spotting system for Russian based on large vocabulary continuous speech recognition. Key algorithms and system settings are described, including the pronunciation variation algorithm, and the experimental results on the real-life telecom data are provided. The description of system architecture and the user interface is provided. The system is based on CMU Sphinx open-source speech recognition platform and on the linguistic models and algorithms developed by Speech Drive LLC. The effective combination of baseline statistic methods, real-world training data, and the intensive use of linguistic knowledge led to a quality result applicable to industrial use.