Biomedical named entity recognition

Overview
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In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Improving the NER’s performance will directly have a positive impact on extracting relations between those entities. In recent years, deep learning has become the main research direction of NER due to the development of effective models. Language transformer models like e.g. BERT are frequently used because they enable the specialisation of models by domain-specific training based on pre-training, yielding models like e.g. BioBERT. However, It is worth investigating the performance of continual models that combine training with specialized and with general corpora against models that were trained from scratch in biomedical literature only. Therefore, in our proposed approach, we combine a specialized training data collection with a modified loss function of the model during training.

**2022 **