Multimodal Named Entity Recognition (MNER) is a pivotal task designed to extract named entities from text with the support of pertinent images. Nonetheless, a notable paucity of data for Chinese MNER has considerably impeded the progress of this natural language processing task within the Chinese domain. Consequently, in this study, we compile a Chinese Multimodal NER dataset (CMNER) utilizing data sourced from Weibo, China's largest social media platform. Our dataset encompasses 5,000 Weibo posts paired with 18,326 corresponding images. The entities are classified into four distinct categories: person, location, organization, and miscellaneous. We perform baseline experiments on CMNER, and the outcomes underscore the effectiveness of incorporating images for NER. Furthermore, we conduct cross-lingual experiments on the publicly available English MNER dataset (Twitter2015), and the results substantiate our hypothesis that Chinese and English multimodal NER data can mutually enhance the performance of the NER model.

翻译：多模态命名实体识别（MNER）是一项关键任务，旨在借助相关图像的支持从文本中提取命名实体。然而，中文MNER数据的显著匮乏极大阻碍了这一自然语言处理任务在中文领域的发展。因此，在本研究中，我们利用来自中国最大社交媒体平台——微博的数据，构建了一个中文多模态命名实体识别数据集（CMNER）。该数据集包含5000条微博帖子及其对应的18326张图像。实体被分为四类：人物、地点、组织和其他。我们在CMNER上进行了基线实验，结果证实了融入图像对命名实体识别的有效性。此外，我们在公开的英文MNER数据集（Twitter2015）上进行了跨语言实验，结果验证了我们的假设：中英文多模态命名实体识别数据可以相互增强NER模型的性能。