  Prompt-based language models have produced encouraging results in numerous
applications, including Named Entity Recognition (NER) tasks. NER aims to
identify entities in a sentence and provide their types. However, the strong
performance of most available NER approaches is heavily dependent on the design
of discrete prompts and a verbalizer to map the model-predicted outputs to
entity categories, which are complicated undertakings. To address these
challenges, we present ContrastNER, a prompt-based NER framework that employs
both discrete and continuous tokens in prompts and uses a contrastive learning
approach to learn the continuous prompts and forecast entity types. The
experimental results demonstrate that ContrastNER obtains competitive
performance to the state-of-the-art NER methods in high-resource settings and
outperforms the state-of-the-art models in low-resource circumstances without
requiring extensive manual prompt engineering and verbalizer design.
