Skip to Main Content

Modelling and Data Analysis
2023. Vol. 13, no. 2, 64–76
https://doi.org/10.17759/mda.2023130203
ISSN: 2219-3758 / 2311-9454 (online)

Problems of Natural Language Classification Using Methods of Classical Machine Learning

 
Audio is AI-generated
276

,

PDF (RUS)
Online translation in english
GS
Information
in Google Scholar
Metrics

Abstract

This article describes the problems of classical machine learning methods in natural language classification. One of these tasks is the classification of structural elements in school essays. On its example, the shortcomings of classical machine learning are considered in comparison with other, more complex algorithms.

General Information

Keywords: text classification, natural language analysis, automation of essay checking

Journal rubric: Data Analysis

OpenAlex citations: 0

OpenAlex trends: Advanced Data Processing Techniques, Advanced Computational Techniques in Science and Engineering, Neural Networks and Applications

Information about the work in OpenAlex

Number of citations: 0

Topics

Advanced Data Processing Techniques

This cluster of papers focuses on the modeling and control of multidimensional systems, with an emphasis on redundant transmission, fuzzy controller adaptation, fault tolerance, real-time systems, and energy efficiency in various domains such as cyber-physical systems, network traffic analysis, and industrial automation.

Number of works: 60256  |  Total number of citations: 206829

Topic detailsв OpenAlex

Advanced Computational Techniques in Science and Engineering

This cluster of papers focuses on the intersection of Internet of Things (IoT) and healthcare systems, including topics such as remote monitoring, algorithmic modeling, wireless sensor networks, telemedicine, biomedical signal processing, and digital information management. The papers also touch upon the application of IoT in smart city healthcare systems and the use of corpus analysis in technical writing classrooms.

Number of works: 19217  |  Total number of citations: 82460

Topic detailsв OpenAlex

Neural Networks and Applications

This cluster of papers covers a wide range of topics related to neural networks, including backpropagation learning, self-organizing maps, radial basis function networks, deep learning, and applications such as pattern classification and function approximation.

Number of works: 253125  |  Total number of citations: 4493057

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2023130203

Received 21.03.2023

Accepted

Published

For citation: Sologub, G.B., Pukhov, V.A. (2023). Problems of Natural Language Classification Using Methods of Classical Machine Learning. Modelling and Data Analysis, 13(2), 64–76. (In Russ.). https://doi.org/10.17759/mda.2023130203

© Sologub G.B., Pukhov V.A., 2023

License: CC BY-NC 4.0

References

  1. Manning, C.D.; Raghavan, P.; Schutze, H. Scoring, term weighting, and the vector space model // Cambridge University Press. 2009 P. 109-133 DOI:10.1017/CBO9780511809071.007
  2. Dyakonov A. G. Lectures https://dyakonov.org/tag/лекции/ (In.Russ,)
  3. Alex Sherstinsky Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network // 2020 P. 1-40 DOI:10.1016/j.physd.2019.132306 Available at: https://sciencedirect.com/science/article/abs/pii/S0167278919305974 (In.Russ,)
  4. Christopher Olah Understanding LSTM Networks // 2015 Available at: http://colah.github.io/posts/2015-08-Understanding-LSTMs/

Information About the Authors

Gleb B. Sologub, Candidate of Science (Physics and Matematics), Associate Professor of the Department of Mathematical Cybernetics of Institute of Information Technologies and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-5657-4826, e-mail: glebsologub@ya.ru

Vyacheslav A. Pukhov, Undergraduate Student of the Institute of Information Technology and Applied Mathematics, Moscow Aviation Institute (National Research University), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-8078-6386, e-mail: csguard26@gmail.com

Metrics

 Web Views

Whole time: 689
Previous month: 16
Current month: 13

 PDF Downloads

Whole time: 276
Previous month: 5
Current month: 17

 Total

Whole time: 965
Previous month: 21
Current month: 30