scholarly journals The Russian Language Corpus and a Neural Network to Analyse Internet Tweet Reports About Covid-19

2021 ◽  
Author(s):  
Alexander Sboev ◽  
Ivan Moloshnikov ◽  
Alexander Naumov ◽  
Anastasia Levochkina ◽  
Roman Rybka
Keyword(s):  
Neural Network ◽  
Russian Language ◽  
Language Corpus ◽  
The Russian Language

Mathematical modeling estimates of the reliability of rumors in mass media

2019 ◽  
Vol 5 (4) ◽  
pp. 181-199
Author(s):  
Alexander A. Chernyaev ◽  
Alexander G. Ivashko
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Mass Media ◽  
Russian Language ◽  
News Article ◽  
News Sources ◽  
Huge Data ◽  
The Neural Network ◽  
High Level ◽  
The Russian Language

One of the most important tasks of the contemporary society includes fighting the spreading false information. The unprecedented transition from the traditional media to the modern methods of receiving news has created many problems with verifying its authenticity. Contemporary journalists have to compete with a huge data stream of ordinary users, which is why the main quality factor is the time to publish a news article. As a result, an increasing number of traditional news sources report unclarified information due to the rush to be first. This paper considers a method for determining the presence of hearing in the mass media for the Russian language. This method aims to study the possibility of searching for rumors among users’ messages in social networks. Achieving this goal requires various methods of text analysis, including semantic and linguistic analysis, as well as the analysis of the distribution of records relative to time segments. During the research, the authors have analyzed different popular tools for obtaining data from social networks. In addition, they have manually compiled and marked a sample for training the neural network. As a tool for solving the problem, we used a neural network based on a multi-layer perceptron. The inputs receive a set of 15 metrics that evaluate all aspects of hearing, and as an output, the probability of hearing. The test was performed using various metrics that showed high results for the constructed neural network model. Cross-validation has shown that the model is able to withstand various checks at a high level.

scholarly journals Metaphors of winter natural phenomena in Lithuanian and Russian poetic texts

2019 ◽  
pp. 1-16
Author(s):  
Birutė Jasiūnaitė ◽  
Jelena Konickaja
Keyword(s):  
Russian Language ◽  
Natural Phenomenon ◽  
Natural Phenomena ◽  
Social Sphere ◽  
The Social ◽  
Russian Poetry ◽  
Poetic Texts ◽  
Living Creature ◽  
Language Corpus ◽  
The Russian Language

The present article is devoted to metaphors of winter natural phenomena, that is frost, ice and hoarfrost, in Lithuanian and Russian poetic texts that mainly come from the 20th century. The metaphors have been identified on the basis of poetry collections, anthologies, children's poetry and the Russian language corpus (363 metaphors in total from 53 Russian and 44 Lithuanian poets’ works). The researchers rely on previous experience in the analysis of metaphors of natural phenomena. Thus, the article considers five groups of metaphors: 1) a natural phenomenon is a living creature or a part of it; 2) frost, ice and hoarfrost are objects (phenomena) of inanimate nature; 3) a winter phenomenon is an object from the social sphere; 4) frost, ice and hoarfrost are abstract objects; 5) some other metaphors. The comparison of the metaphors in two poetic languages has shown both significant similarities and striking differences. The similarity consists in the fact that subject metaphors are most often utilized in poetic texts, as well as anthropomorphic, zoomorphic and biomorphic metaphors. The differences are explained by the lack of metaphors in one of the systems that are presented in the other one, for instance, in Lithuanian poetry there are no metaphors of ‘frost’ as smoke and ‘ice’ as mica, while Russian poets do not use metaphors, such as ‘a winter phenomenon of nature’ is a means for lighting, or ‘icicles’ are a clock that is characteristic of Lithuanian poetry. In Russian poetry, there is a branching group where ‘a winter phenomenon’ is metal, a precious stone, and in Lithuanian poetry there is a group of ‘frost (ice, hoarfrost)’ that is a sharp cutting object. The differences between the two poetic systems are also associated with connotations: in Russian poetry, unlike Lithuanian, metaphors of winter natural phenomena quite often have positive connotations. At the end of the article, a scheme is presented that reflects the results of the analysis.

scholarly journals Encoder-decoder models for recognition of Russian speech

2019 ◽  
pp. 45-53
Author(s):  
Nikita Markovnikov ◽  
Irina Kipyatkova
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Recognition Accuracy ◽  
Language Model ◽  
Hybrid Models ◽  
Attention Mechanism ◽  
Russian Language ◽  
End To End ◽  
The Russian Language ◽  
Decoding Speed

Problem: Classical systems of automatic speech recognition are traditionally built using an acoustic model based on hidden Markovmodels and a statistical language model. Such systems demonstrate high recognition accuracy, but consist of several independentcomplex parts, which can cause problems when building models. Recently, an end-to-end recognition method has been spread, usingdeep artificial neural networks. This approach makes it easy to implement models using just one neural network. End-to-end modelsoften demonstrate better performance in terms of speed and accuracy of speech recognition. Purpose: Implementation of end-toendmodels for the recognition of continuous Russian speech, their adjustment and comparison with hybrid base models in terms ofrecognition accuracy and computational characteristics, such as the speed of learning and decoding. Methods: Creating an encoderdecodermodel of speech recognition using an attention mechanism; applying techniques of stabilization and regularization of neuralnetworks; augmentation of data for training; using parts of words as an output of a neural network. Results: An encoder-decodermodel was obtained using an attention mechanism for recognizing continuous Russian speech without extracting features or usinga language model. As elements of the output sequence, we used parts of words from the training set. The resulting model could notsurpass the basic hybrid models, but surpassed the other baseline end-to-end models, both in recognition accuracy and in decoding/learning speed. The word recognition error was 24.17% and the decoding speed was 0.3 of the real time, which is 6% faster than thebaseline end-to-end model and 46% faster than the basic hybrid model. We showed that end-to-end models could work without languagemodels for the Russian language, while demonstrating a higher decoding speed than hybrid models. The resulting model was trained onraw data without extracting any features. We found that for the Russian language the hybrid type of an attention mechanism gives thebest result compared to location-based or context-based attention mechanisms. Practical relevance: The resulting models require lessmemory and less speech decoding time than the traditional hybrid models. That fact can allow them to be used locally on mobile deviceswithout using calculations on remote servers.

scholarly journals Development of a word frequency lists and textscorpus of russian pre-reform language

2020 ◽  
pp. 31-42
Author(s):  
Anna Troshina ◽  
Nikolay Ershov
Keyword(s):  
Programming Languages ◽  
Web Application ◽  
Word List ◽  
Russian Language ◽  
Software Implementation ◽  
Frequency Word ◽  
Electronic Dictionary ◽  
Electronic Frequency ◽  
Language Corpus ◽  
The Russian Language

The paper is devoted to the development of the Russian language corpus in pre-reform spelling and the development of a frequency word list based on this corpus of the Russian language of the 18th - early 20th centuries. Existing approaches to solving this problem are considered and analyzed, including an overview of a number of the most popular electronic national corpuses – Russian, British and Czech. The model of the internal organization of the electronic frequency word list and its functionality are formulated. The software implementation of the Russian pre-reform language corpus and the frequency word list based on it is described using the programming languages Python and Javascript and the Mongo DB database. The issues of web application implementation for access to the developed electronic dictionary are considered.

A BRIEF ANALYSIS OF TIME-PREPOSITIONS IN THE RUSSIAN LANGUAGE

2019 ◽  
Vol 18 (28) ◽  
pp. 187-195
Author(s):  
Yabing Zhang
Keyword(s):  
Foreign Language ◽  
Foreign Students ◽  
Russian Language ◽  
Parts Of Speech ◽  
Organic Structure ◽  
Part Of Speech ◽  
True Meaning ◽  
Oral Speech ◽  
The Russian Language

This article is devoted to the problem of using Russian time-prepositions by foreigners, especially by the Chinese. An analysis of modern literature allows the author to identify the main areas of the work aimed at foreign students’ development of the skills and abilities to correctly build the prepositional combinations and continuously improve the communication skills by means of the Russian language. In this paper, the time-prepositions in the Russian language have been analyzed in detail; some examples of polysemantic use of prepositions, their semantic and stylistic shades alongside with possible errors made by foreign students are presented. The results of the study are to help in developing a system of teaching Russian time-prepositions to a foreign language audience, taking into account their native language, on the basis of the systemic and functional, communicative and activity-centred basis. The role of Russian time-prepositions in constructing word combinations has been identified; the need for foreign students’ close attention to this secondary part of speech has been specified. It has been stated that prepositions are the most dynamic and open type of secondary language units within the quantitative and qualitative composition of which regular changes take place. The research substantiates the need that students should be aware of the function of time-preposition in speech; they are to get acquainted with the main time-prepositions and their meanings, to distinguish prepositions and other homonymous parts of speech as well as to learn stylistic shades of time-prepositions. Some recommendations related to the means of mastering time-prepositions have been given: to target speakers to assimilate modern literary norms and, therefore, to teach them how to choose and use them correctly by means of linguistic keys that are intended to fill the word with true meaning, to give it an organic structure, an inherent form and an easy combinability in the texts and oral speech.

USE OF INNOVATIVE TEACHING METHODS IN RUSSIAN LESSONS IN GROUPS WITH A NON-RUSSIAN TEACHING LANGUAGE

2020 ◽  
Vol 6 (3) ◽  
pp. 204-212
Author(s):  
Nigora Vokhidova ◽  
Keyword(s):  
Foreign Language ◽  
Teaching Methods ◽  
Group Work ◽  
Russian Language ◽  
Interactive Communication ◽  
Innovative Teaching ◽  
Teaching Language ◽  
New Methods ◽  
The Russian Language

The article discusses the effectiveness of innovative approaches in teaching Russian as a foreign language. It is noted that the use of new methods makes it possible to take into account the knowledge already acquired by the student for studying the Russian language and developing creative skills. The role of such a form of training as group work is shown, and some methods of interactive communication between students in practical classes in the Russian language are considered

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