This study focuses on the generation of Persian named entity datasets through
the application of machine translation on English datasets. The generated
datasets were evaluated by experimenting with one monolingual and one
multilingual transformer model. Notably, the CoNLL 2003 dataset has achieved
the highest F1 score of 85.11%. In contrast, the WNUT 2017 dataset yielded the
lowest F1 score of 40.02%. The results of this study highlight the potential of
machine translation in creating high-quality named entity recognition datasets
for low-resource languages like Persian. The study compares the performance of
these generated datasets with English named entity recognition systems and
provides insights into the effectiveness of machine translation for this task.
Additionally, this approach could be used to augment data in low-resource
language or create noisy data to make named entity systems more robust and
improve them.

通过将机器翻译应用于英语数据集，本研究聚焦于生成波斯语命名实体数据集。通过实验评估，最高的 F1 分数是 CoNLL 2003 数据集的 85.11％。本研究的结果强调了机器翻译在为低资源语言（如波斯语）创建高质量的命名实体识别数据集方面的潜力，并提供了有关机器翻译在此任务中有效性的见解。此外，此方法可用于增强低资源语言中的数据或创建嘈杂数据以使命名实体系统更加稳健并改进它们。