We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10% on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.

翻译：我们报告了SemEval-2023任务二“细粒度多语言命名实体识别（MultiCoNER 2）”的研究成果。该任务划分为13个赛道，重点研究在单语和多语言场景及噪声条件下，识别12种语言中复杂细粒度命名实体（如WRITTENWORK、VEHICLE、MUSICALGRP等）的方法。任务使用MultiCoNER V2数据集，包含孟加拉语、中文、英语、波斯语、法语、德语、印地语、意大利语、葡萄牙语、西班牙语、瑞典语和乌克兰语的220万条实例。MultiCoNER 2是SemEval-2023最热门的任务之一，吸引了47支团队的842份提交，其中34支团队提交了系统论文。结果表明，媒体标题和产品名称等复杂实体类型最具挑战性。将外部知识融入Transformer模型的方法取得了最佳性能，其中创意作品类和团体类的提升最为显著，但即使引入外部知识，这两类实体仍具挑战性。部分细粒度类别（如SCIENTIST、ARTWORK、PRIVATECORP）的识别难度高于其他类别。我们还发现噪声数据对模型性能影响显著，在噪声子集上平均性能下降10%。该任务凸显了未来需加强复杂实体噪声数据场景下NER鲁棒性研究的重要性。