This study explored the usability of prompt generation on named entity
recognition (NER) tasks and the performance in different settings of the
prompt. The prompt generation by GPT-J models was utilized to directly test the
gold standard as well as to generate the seed and further fed to the RoBERTa
model with the spaCy package. In the direct test, a lower ratio of negative
examples with higher numbers of examples in prompt achieved the best results
with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the
F1 score, in all settings after training with the RoBERTa model. The study
highlighted the importance of seed quality rather than quantity in feeding NER
models. This research reports on an efficient and accurate way to mine clinical
notes for periodontal diagnoses, allowing researchers to easily and quickly
build a NER model with the prompt generation approach.

这项研究探讨了提示生成在命名实体识别（NER）任务中的可用性以及在不同设置下的性能。研究强调了种子质量而非数量对于给 NER 模型提供输入的重要性。