  Named entity recognition is a key component of Information Extraction (IE),
particularly in scientific domains such as biomedicine and chemistry, where
large language models (LLMs), e.g., ChatGPT, fall short. We investigate the
applicability of transfer learning for enhancing a named entity recognition
model trained in the biomedical domain (the source domain) to be used in the
chemical domain (the target domain). A common practice for training such a
model in a few-shot learning setting is to pretrain the model on the labeled
source data, and then, to finetune it on a hand-full of labeled target
examples. In our experiments, we observed that such a model is prone to
mislabeling the source entities, which can often appear in the text, as the
target entities. To alleviate this problem, we propose a model to transfer the
knowledge from the source domain to the target domain, but, at the same time,
to project the source entities and target entities into separate regions of the
feature space. This diminishes the risk of mislabeling the source entities as
the target entities. Our model consists of two stages: 1) entity grouping in
the source domain, which incorporates knowledge from annotated events to
establish relations between entities, and 2) entity discrimination in the
target domain, which relies on pseudo labeling and contrastive learning to
enhance discrimination between the entities in the two domains. We conduct our
extensive experiments across three source and three target datasets,
demonstrating that our method outperforms the baselines by up to 5% absolute
value.
