Comparative analysis identifies neural network architectures' effectiveness in extracting semantic information for text data, suggesting optimal approaches for text analysis tasks.
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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Rodnin et al. (2025) studied this question.
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