  Supervised named entity recognition (NER) in the biomedical domain depends on
large sets of annotated texts with the given named entities. The creation of
such datasets can be time-consuming and expensive, while extraction of new
entities requires additional annotation tasks and retraining the model. To
address these challenges, this paper proposes a method for zero- and few-shot
NER in the biomedical domain. The method is based on transforming the task of
multi-class token classification into binary token classification and
pre-training on a large amount of datasets and biomedical entities, which allow
the model to learn semantic relations between the given and potentially novel
named entity labels. We have achieved average F1 scores of 35.44% for zero-shot
NER, 50.10% for one-shot NER, 69.94% for 10-shot NER, and 79.51% for 100-shot
NER on 9 diverse evaluated biomedical entities with fine-tuned PubMedBERT-based
model. The results demonstrate the effectiveness of the proposed method for
recognizing new biomedical entities with no or limited number of examples,
outperforming previous transformer-based methods, and being comparable to
GPT3-based models using models with over 1000 times fewer parameters. We make
models and developed code publicly available.
