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.

通过从中国最大的社交媒体平台微博获取数据，我们编制了一个包含 5000 个微博帖子和 18326 个对应图片的中文多模态命名实体识别数据集（CMNER）。我们在 CMNER 上进行了基准实验，结果表明将图像与 NER 相结合的有效性。此外，我们还在公开的英文多模态命名实体识别数据集（Twitter2015）上进行了跨语言实验，结果证实了中文和英文多模态 NER 数据可以相互增强 NER 模型的性能。