COMPARISON OF NEURAL NETWORK ARCHITECTURES FOR TEXT DATA ANALYSIS

The article presents a comparative analysis of neural network architectures for processing text data. The features of various models (RNN, CNN, transformers) and their learning algorithms are considered, as well as their effectiveness in extracting semantic information from large text corpora. The main focus is on identifying the advantages and disadvantages of each architecture to optimize text analysis tasks.

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Published
2025-08-25
DOI
https://doi.org/10.58168/phbsc-it2025_209-214
Primary Topic
Neural Networks and Applications
Type
article
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article

COMPARISON OF NEURAL NETWORK ARCHITECTURES FOR TEXT DATA ANALYSIS

V. Gorbunov, A. Rodnin
Neural Networks and Applications
article

COMPARISON OF NEURAL NETWORK ARCHITECTURES FOR TEXT DATA ANALYSIS

V. Gorbunov, A. Rodnin
article en

Abstract

The article presents a comparative analysis of neural network architectures for processing text data. The features of various models (RNN, CNN, transformers) and their learning algorithms are considered, as well as their effectiveness in extracting semantic information from large text corpora. The main focus is on identifying the advantages and disadvantages of each architecture to optimize text analysis tasks.

Vologda State University (RU)
Openalex Percentile: Top 89%
Neural Networks and Applications
DOIOpen Access PDFOpenAlex
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COMPARISON OF NEURAL NETWORK ARCHITECTURES FOR TEXT DATA ANALYSIS — V. Gorbunov, A. Rodnin · (2025) | TGRS Research Map | TGRS