  The challenge of recognizing named entities in a given text has been a very
dynamic field in recent years. This is due to the advances in neural network
architectures, increase of computing power and the availability of diverse
labeled datasets, which deliver pre-trained, highly accurate models. These
tasks are generally focused on tagging common entities, but domain-specific
use-cases require tagging custom entities which are not part of the pre-trained
models. This can be solved by either fine-tuning the pre-trained models, or by
training custom models. The main challenge lies in obtaining reliable labeled
training and test datasets, and manual labeling would be a highly tedious task.
  In this paper we present PharmKE, a text analysis platform focused on the
pharmaceutical domain, which applies deep learning through several stages for
thorough semantic analysis of pharmaceutical articles. It performs text
classification using state-of-the-art transfer learning models, and thoroughly
integrates the results obtained through a proposed methodology. The methodology
is used to create accurately labeled training and test datasets, which are then
used to train models for custom entity labeling tasks, centered on the
pharmaceutical domain. The obtained results are compared to the fine-tuned BERT
and BioBERT models trained on the same dataset. Additionally, the PharmKE
platform integrates the results obtained from named entity recognition tasks to
resolve co-references of entities and analyze the semantic relations in every
sentence, thus setting up a baseline for additional text analysis tasks, such
as question answering and fact extraction. The recognized entities are also
used to expand the knowledge graph generated by DBpedia Spotlight for a given
pharmaceutical text.
