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.

该论文提出了 ContrastNER，一个基于提示的命名实体识别框架，使用离散和连续的标记来学习对话。通过对比学习的方法，ContrastNER 在高资源设置中获得了与现有技术同样的性能，并在低资源环境中胜过现有模型。