Abstract: The rapid development of the information space urges the named entities recognition, particularly financial ones. The high accuracy of detection of such entities allows to significantly improve the quality of data analysis in the financial sector. Therefore, this study aimed to develop and evaluate the effectiveness of a hybrid model that combines modern natural language processing methods for recognizing financially named entities in texts in the Ukrainian language. The following methods are used for this purpose: data analysis, modeling, experimental method, and comparative analysis. CRF, BiLSTM, and BERT models, as well as their combination, are applied for the recognition of named entities. The experiments demonstrated that the hybrid model proved to be effective for recognizing financially named entities, evidencing an advantage over traditional methods. The average accuracy indicators of the model are: Precision at 94%, Recall at 93%, F1-Score at 94%, and Accuracy at 94%. This emphasizes the appropriateness of using complex models for analyzing domain-specific texts. Research prospects include the improvement of hybrid models and their adaptation to other languages and mixed data.

Keywords: named entity recognition, financial text analysis, natural language processing, financial named entities, hybrid model, CRF, BiLSTM, BERT

Pages: 569-581