Challenges in Named Entity Recognition for Russian-Language Datasets
Abstract
Challenges in Named Entity Recognition for Russian-Language Datasets
Incoming article date: 13.07.2025This article discusses the implementation features of named entity recognition models. In the course of the work, a number of experiments were conducted with both traditional models and well-known neural network architectures, a hybrid model, the features of the results, their comparison and possible explanations are considered. In particular, it is shown that a hybrid model with the addition of a bidirectional long short-term memory can give better results than the basic bidirectional representation model based on transformers. It is also shown that, improved by adding a thinning layer for regularization, a weighted loss function and a linear classifier on top of the outputs, a bidirectional representation model based on transformers can give high metric values. For clarity, the work provides graphs of model training and tables with metrics for comparison. In the process of work, conclusions and recommendations were formed.
Keywords: text analysis, artificial intelligence, named entity recognition, neural networks, deep learning, machine learning