Abstract:

By the radiosounding of the ionosphere with short-wave signals, can be obtained information about the processes in the ionospheric plasma, about its structure and state; these data are also extremely important for radio engineering systems operating in the short-wave range. To date, a large amount of experimental data has been accumulated for various geo- and heliophysical, spatial and temporal conditions. The interest in large amounts of ionospheric radiosonde data is also motivated by the possibility of constructing statistical models using machine learning theory methods. The paper presents some Internet resources with ionospheric radiosonde data, shows the prospects for their application, and also identifies some problems, such as insufficient documentation of some data formats and the presentation of ionograms only in the form of raster images, most of which are also scanned from photographic films.

Abstract: This paper describes the morphological disambiguation methods and their application for the Tatar language. The state-of-the art technology is discussed. We analyze the contextual and statistical methods and their evaluations for different languages.

Abstract:

The results of studies of atmospheric pressure in the Arctic region of Russia in the period from 1948 to 2008 are presented. The analysis of the climatic seasonal variation of the atmospheric pressure fields is carried out. As the main research method, the probabilistic and statistical analysis of the time series of the pressure field 60 years long at fixed points in the region of the Arctic zone of Russia was used. In total, about 90,000 daily (in six-hour increments) pressure values were examined. On the basis of these data, a climatic seasonal variation was constructed as an averaging of the values of a given time series at each point in space and for a fixed date. The characteristics of the seasonal course, its amplitude and phase have been studied. These characteristics were analyzed and their geophysical interpretation was carried out. In particular, the minimum and maximum values ​​of the series were determined for the entire region and the time series of these characteristics were constructed. It is shown that the deviation is asymmetric, this is an unobvious research result. For the maximum and minimum, the best approximations were constructed, and these approximations were tested by known methods of statistical analysis, including maximum likelihood, least squares and goodness of fit methods (tests), in particular, the χ²-criterion. The conducted research has applications both purely physical (allows to explain the nature, genesis and distribution of large-scale atmospheric formations in a climatic year) and prognostic (allows understanding and tracking trends in climate, as well as quantitatively assessing the scale and variability of large-scale atmospheric processes). Numerical calculations were performed on the Lomonosov-2 supercomputer of the Lomonosov Moscow State University.

Abstract:

The paper describes our approach to the task of sentiment analysis of tweets within SentiRuEval – an open evaluation of sentiment analysis systems for the Russian language. We took part in the task of sentiment analysis of Russian tweets concerning two types of organizations: banks and telecommunications companies. On both datasets, the participants were required to perform a three-way classification of tweets: positive, negative or neutral.

We used various statistical methods as basis for our machine learning algorithms. Linguistic features produced by our morpho-syntactic analyzer are applied to the classification. Syntactic relations proved to be a crucial feature for any statistical method evaluated, and SVM-based classification performed better than the others. Normalized words are another important feature for the algorithm.

The evaluation revealed that our method proved to be rather successful: we scored the first in three out of four evaluation measures.