23 research outputs found
Mechanisms of Precedent Statements Adaptation in Chinese Internet Communication
ΠΠΎΡΡΡΠΏΠΈΠ»Π° Π² ΡΠ΅Π΄Π°ΠΊΡΠΈΡ 28.05.2017. ΠΡΠΈΠ½ΡΡΠ° ΠΊ ΠΏΠ΅ΡΠ°ΡΠΈ 18.04.2018.Submitted on 28 May, 2017. Accepted on 18 April, 2018.Π‘ΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΡΠ΅ΠΊΡΡΡ Π½Π°ΡΡΡΠ΅Π½Ρ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΠΌΠΈ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡΠΌΠΈ, Π½Π΅ ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ ΠΈ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΏΡΠΎΡΡΡΠ°Π½ΡΡΠ²ΠΎ, Π² ΡΠ°ΡΡΠ½ΠΎΡΡΠΈ ΠΊΠΈΡΠ°ΠΉΡΠΊΠ°Ρ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΡ. ΠΠ΄Π½Π°ΠΊΠΎ Π½Π΅ Π²ΡΠ΅ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΠΈ ΡΠ΅ΡΠΈ ΠΌΠΎΠ³ΡΡ ΠΎΠ±Π½Π°ΡΡΠΆΠΈΡΡ Π² ΡΠ΅ΠΊΡΡΠ΅ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΠΎΠ΅ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠ΅, ΠΎΡΠ΅Π½ΠΈΡΡ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ Π΅Π³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ°ΡΠΏΠΎΠ·Π½Π°ΡΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΠΎΡΡΠΈ. ΠΒ Π΄Π°Π½Π½ΠΎΠΉ ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΠΏΡΠΈΠ½ΡΡΠ° ΠΏΠΎΠΏΡΡΠΊΠ° ΡΠ°ΡΡΠΌΠΎΡΡΠ΅ΡΡ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΠ΅ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΈΡΡΠΎΡΠ½ΠΈΠΊ ΠΏΠΎΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΡΠ°Π·Π΅ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΊΠΈΡΠ°ΠΉΡΠΊΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠ°, ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠΎΡΠΌΠ°Π»ΡΠ½ΡΠ΅ ΠΈ ΡΠΌΡΡΠ»ΠΎΠ²ΡΠ΅ ΡΠΏΠΎΡΠΎΠ±Ρ ΠΈΡ
Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΈΠ·ΡΡΠΈΡΡ ΡΡΠ°Π·Π΅ΠΎΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡ
Π΅ΠΌΡ, ΠΏΠΎΡΡΡΠΎΠ΅Π½Π½ΡΠ΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΉ. Π ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΉ Π² ΠΊΠΈΡΠ°ΠΉΡΠΊΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΈ Π²ΡΡΡΡΠΏΠ°ΡΡ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
Π΄Π΅ΡΡΠ΅Π»Π΅ΠΉ, Π²Π΅Π΄ΡΡΠΈΡ
, ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎΠ², Π·Π°ΠΏΠΈΡΠΈ ΠΈΠ»ΠΈ ΠΊΠΎΠΌΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»Π΅ΠΉ ΠΈ Ρ. Π΄. Π’Π΅ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΡΠ΅ Π±Π΅Π· ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΡΠΎΡΠΌΡ ΠΏΡΠΈ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΌ ΡΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠΈ Π² ΠΈΠ½ΡΠ΅ΡΠ½Π΅ΡΠ΅ ΠΏΡΠΈΠ±ΡΠ΅Π»ΠΈ Π½ΠΎΠ²ΠΎΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΈ ΡΡΠ°Π»ΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΉ, ΠΌΠΎΠΆΠ½ΠΎ ΠΎΡΠ½Π΅ΡΡΠΈ ΠΊ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΡΠ°Π·Π΅ΠΎΠ»ΠΎΠ³ΠΈΠ·ΠΌΠ°ΠΌ. Π’Π΅ ΠΆΠ΅, Π² ΠΊΠΎΡΠΎΡΡΡ
ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ Π·Π°ΠΌΠ΅Π½Π° Π»Π΅ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ², ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΎΡΠ½Π΅ΡΠ΅Π½Ρ ΠΊ ΡΡΠ°Π·Π΅ΠΎΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΡ
Π΅ΠΌΠ°ΠΌ (ΡΡΠ°Π·Π΅ΠΎΡΡ
Π΅ΠΌΠ°ΠΌ), Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠΎΡΠΎΡΡΡ
Π²ΡΡΡΡΠ°ΠΈΠ²Π°ΡΡΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈΡ. ΠΠ΅ΡΠ΅Π΄ΠΊΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΠ°Π·Π΅ΠΎΠ»ΠΎΠ³ΠΈΠ·ΠΌΠΎΠ² ΠΈ ΡΡΠ°Π·Π΅ΠΎΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡ
Π΅ΠΌ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π° ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡΡ
, ΠΏΡΠΎΠ΄ΠΈΠΊΡΠΎΠ²Π°Π½ΠΎ ΡΡΡΠ΅ΠΌΠ»Π΅Π½ΠΈΠ΅ΠΌ ΠΏΡΠΈΠ΄Π°ΡΡ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ ΡΠΌΠΎΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΎΡΡΠ΅Π½ΠΎΠΊ, ΠΎΠ΄Π½Π°ΠΊΠΎ Π΅ΡΠ»ΠΈ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠ΅ Π·Π°ΡΡΠ°Π³ΠΈΠ²Π°Π΅Ρ, Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΡΠΏΠ΅ΠΊΡ, ΠΈΡ
ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠΎΡΠΎΠ±ΠΎΠΌ ΠΎΠ±Ρ
ΠΎΠ΄Π° Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΠΆΠ΅ΡΡΠΊΠΎΠΉ Π² ΠΠΈΡΠ°Π΅ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅Π½Π·ΡΡΡ. Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ Π°Π΄Π°ΠΏΡΠ°ΡΠΈΠΈ ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΉ ΠΊ ΠΈΡ
Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΌΡ ΡΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ. Π ΡΠ°ΠΊΠΈΠΌ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ°ΠΌ Π°Π²ΡΠΎΡ ΠΎΡΠ½ΠΎΡΠΈΡ ΡΡΡΡΠΊΡΡΡΠ½ΡΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠ΅, ΡΡΠ΅Π΄ΠΈ ΠΏΡΠΎΡΠ΅Π³ΠΎ, Π΅Π³ΠΎ ΡΠΌΡΡΠ»ΠΎΠ²ΡΡ ΠΈ ΡΠΈΠ½ΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΡΡ Π²Π°ΡΠΈΠ°ΡΠΈΠ²Π½ΠΎΡΡΡ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΉ ΡΠ΅ΠΌΠ°Π½ΡΠΈΠ·Π°ΡΠΈΠΈ Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΡ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°ΡΡ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡΠ΅ΡΠ΅Π΄Π΅Π½ΡΠ½ΡΡ
Π²ΡΡΠΊΠ°Π·ΡΠ²Π°Π½ΠΈΠΉ, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ
Π² ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΊΠΈΡΠ°Π΅ΡΠ·ΡΡΠ½ΠΎΠΉ ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΠΊΠΎΠΌΠΌΡΠ½ΠΈΠΊΠ°ΡΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°ΡΡ Π²ΠΎΠΏΡΠΎΡΡ, ΡΡΠ΅Π±ΡΡΡΠΈΠ΅ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅Π³ΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ.Modern texts contain many precedent statements, and Internet space is not an exception, Chinese Internet communication in particular. However, not all Internet users can detect precedent statements in texts, measure their features, and determine the sources of precedent statements. This article is an attempt to study precedent statements as sources of modern Chinese Internet phraseology, and also analyse their formal and semantic ways of adaptation, and examine phraseosyntactic schemes (phraseoschemes) based on precedent statements. Utterances of well-known figures, famous TV hosts, politicians, as well as posts or comments of Internet users may become precedent statements in Chinese Internet communication. Statements that acquire new meanings without changing their form and begin to function as precedent statements can be considered syntactic phraseological units, while statements with replaced lexical components can be considered phraseoschemes, underlying some sentences. Quite frequently, the use of syntactic phraseological units and phraseoschemes based on precedent statements is a response to the desire to add humorous tone to an utterance, however, if it touches upon, for example, a political aspect, the use of precedent statements is also a method of bypassing quite rigid Internet censorship in China. The article examines mechanisms of precedent statements adaptation to their secondary usage. According to the author, such mechanisms include structural transformations of the original statement, which provide, among other things, its semantic and syntactic variability, and include a secondary semantisation of the statement. The analysis allows the author to demonstrate the diversity of types of precedent statements used in modern Chinese Internet communication, and formulate questions that require further study
Π Π΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΡΠ΅ ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π½Π°Π΄ ΠΏΠΎΡΡΠΈΠΊΠΎΠ»ΡΡΠ°ΠΌΠΈ
Periods and statistics of linear recurrence sequences over near-rings generated by endomorphisms of finite non-abelian extra-spexdal 2-groups are investigatexl.ΠΡΡΠ»Π΅Π΄ΡΡΡΡΡ ΠΏΠ΅ΡΠΈΠΎΠ΄Ρ ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ²ΠΎΠΉΡΡΠ²Π° Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ
ΡΠ΅ΠΊΡΡΡΠ΅Π½ΡΠ½ΡΡ
ΠΏΠΎΡΠ»Π΅Π΄ΠΎΠ²Π°ΡΠ΅Π»ΡΠ½ΠΎΡΡΠ΅ΠΉ Π½Π°Π΄ ΠΏΠΎΡΡΠΈΠΊΠΎΠ»ΡΡΠ°ΠΌΠΈ, ΠΏΠΎΡΠΎΠΆΠ΄ΡΠ½Π½ΡΠΌΠΈ ΡΠ½Π΄ΠΎΠΌΠΎΡΡΠΈΠ·ΠΌΠ°ΠΌΠΈ ΠΊΠΎΠ½Π΅ΡΠ½ΡΡ
Π½Π΅Π°Π±Π΅Π»Π΅Π²ΡΡ
ΡΠΊΡΡΡΠ°ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΡΡ
2-Π³ΡΡΠΏΠΏ
Strict baselines for Covid-19 forecasting and ML perspective for USA and Russia
Currently, the evolution of Covid-19 allows researchers to gather the
datasets accumulated over 2 years and to use them in predictive analysis. In
turn, this makes it possible to assess the efficiency potential of more complex
predictive models, including neural networks with different forecast horizons.
In this paper, we present the results of a consistent comparative study of
different types of methods for predicting the dynamics of the spread of
Covid-19 based on regional data for two countries: the United States and
Russia. We used well-known statistical methods (e.g., Exponential Smoothing), a
"tomorrow-as-today" approach, as well as a set of classic machine learning
models trained on data from individual regions. Along with them, a neural
network model based on Long short-term memory (LSTM) layers was considered, the
training samples of which aggregate data from all regions of two countries: the
United States and Russia. Efficiency evaluation was carried out using
cross-validation according to the MAPE metric. It is shown that for complicated
periods characterized by a large increase in the number of confirmed daily
cases, the best results are shown by the LSTM model trained on all regions of
both countries, showing an average Mean Absolute Percentage Error (MAPE) of
18%, 30%, 37% for Russia and 31%, 41%, 50% for US for predictions at forecast
horizons of 14, 28, and 42 days, respectively
A deep learning method based on language models for processing natural language Russian commands in human robot interaction
This paper describes the transformation process complex Russian-speaking natural language commands into a formalized graph RDF format for interaction with the robotic platfor
A scalable framework for stylometric analysis of multi-author documents
This is an accepted manuscript of a chapter published by Springer in Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science, vol 10827 on 13/05/2018, available online: https://doi.org/10.1007/978-3-319-91452-7_52
The accepted version of the publication may differ from the final published version.Stylometry is a statistical technique used to analyze the variations in the authorβs writing styles and is typically applied to authorship attribution problems. In this investigation, we apply stylometry to authorship identification of multi-author documents (AIMD) task. We propose an AIMD technique called Co-Authorship Graph (CAG) which can be used to collaboratively attribute different portions of documents to different authors belonging to the same community. Based on CAG, we propose a novel AIMD solution which (i) significantly outperforms the existing state-of-the-art solution; (ii) can effectively handle a larger number of co-authors; and (iii) is capable of handling the case when some of the listed co-authors have not contributed to the document as a writer. We conducted an extensive experimental study to compare the proposed solution and the best existing AIMD method using real and synthetic datasets. We show that the proposed solution significantly outperforms existing state-of-the-art method
Recurrence sequences over near-rings
Periods and statistics of linear recurrence sequences over near-rings generated by endomorphisms of finite non-abelian extra-spexdal 2-groups are investigatexl
Mobile medical unit as a rational model of modern mobile medical formation operating in the metropolis in emergency situations
Purpose of the study. Substantiation of organizational principles of development of the health system of the population of the megalopolis in emergency situations. The article analyzes the stages of creation, testing and practical implementation of the modern organizational and staff structure of mobile medical formation and its functioning to meet the needs of the peaceful population of Moscow in Moscow pre-hospital period (2013-2017).Materials and methods. A survey of 112 respondents from 57 industry-specific metropolitan organizations and enterprises of the state healthcare system was conducted on the main indicators of the activities of institutions involved in the formation of a mobile medical unit. The result of this questionnaire was an assessment of the personnel potential of outpatient polyclinic establishments-shapers of mobile medical units, as well as the provision of full-time property and material resources of organizations and enterprises of the Moscow City Health Department involved in their formation. 24 research exercises were conducted on the formation and use of mobile medical teams to eliminate the health consequences of emergencies.Results. The article considers the issues of resource support of organizations and enterprises of the state healthcare system of Moscow. The calculation of the internal reserves of outpatient clinics of the Moscow Department of Health has been prepared, reflecting the personnel potential of the medical organizations that form the units. Research exercises have been carried out on the formation of mobile medical units, the issuance of medical equipment and sanitary equipment for them from medical warehouses, as well as the functioning of the units of mobile medical units during the mass arrival of people of various profiles.Conclusion. The study showed that the organizational foundations in the field of medical support for the population in emergencies make it possible to ensure the formation of a mobile medical unit with labor and material resources with the maximum use of internal reserves of regional health care. The proposed model of the organizational structure of a modern medical formation is an alternative to the bulky and inactive first aid unit that has existed for more than 40 years
On the applicability of STDP-based learning mechanisms to spiking neuron network models
The ways to creating practically effective method for spiking neuron networks learning, that would be appropriate for implementing in neuromorphic hardware and at the same time based on the biologically plausible plasticity rules, namely, on STDP, are discussed. The influence of the amount of correlation between input and output spike trains on the learnability by different STDP rules is evaluated. A usability of alternative combined learning schemes, involving artificial and spiking neuron models is demonstrated on the iris benchmark task and on the practical task of gender recognition
Extraction of the relations among significant pharmacological entities in Russian-language reviews of internet users on medications
Nowadays, the analysis of digital media aimed at prediction of the societyβs reaction to particular events and processes is a task of a great significance. Internet sources contain a large amount of meaningful information for a set of domains, such as marketing, author profiling, social situation analysis, healthcare, etc. In the case of healthcare, this information is useful for the pharmacovigilance purposes, including re-profiling of medications. The analysis of the mentioned sources requires the development of automatic natural language processing methods. These methods, in turn, require text datasets with complex annotation including information about named entities and relations between them. As the relevant literature analysis shows, there is a scarcity of datasets in the Russian language with annotated entity relations, and none have existed so far in the medical domain. This paper presents the first Russian-language textual corpus where entities have labels of different contexts within a single text, so that related entities share a common context. therefore this corpus is suitable for the task of belonging to the medical domain. Our second contribution is a method for the automated extraction of entity relations in Russian-language texts using the XLM-RoBERTa language model preliminarily trained on Russian drug review texts. A comparison with other machine learning methods is performed to estimate the efficiency of the proposed method. The method yields state-of-the-art accuracy of extracting the following relationship types: ADRβDrugname, DrugnameβDiseasename, DrugnameβSourceInfoDrug, DiseasenameβIndication. As shown on the presented subcorpus from the Russian Drug Review Corpus, the method developed achieves a mean F1-score of 80.4% (estimated with cross-validation, averaged over the four relationship types). This result is 3.6% higher compared to the existing language model RuBERT, and 21.77% higher compared to basic ML classifiers