Named entity recognition is one of the tasks of the natural language processing problem. It is about tagging words and phrases with labels of predefined
types of named entities. Examples of named entity recognition use cases
are content classification for news providers, efficient search algorithms, content recommendation, organization of research papers and customer support. We have studied the problem of named entity recognition on domain texts from pharmacy. For this purpose, we used four different named entity recognition methods using two corpora (CHEMDNER and n2c2) that contain manually annotated named entities from the pharmacy domain. We also evaluated the models on texts, which we manually annotated. The BERT model performed best. For practical use, it is probably necessary to put some more effort in the model in order to improve it.
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