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.

该研究介绍了 SemEval-2023 Task 2 的发现，该任务主要聚焦于跨越 12 种语言的复杂名词实体的识别方法（如 WRITTENWORK，VEHICLE，MUSICALGRP），并研究了如何在单语和多语境以及噪声情况下实现。MultiCoNER 2 是 SemEval-2023 中最受欢迎的任务之一，从 47 个团队的 842 篇提交的论文中脱颖而出，并发现了媒体标题和产品名称是最具挑战性的实体类型，提出了将外部知识融入 transformer 模型中来实现最佳表现的方法，并注意到嘈杂的数据对模型性能有重要影响，对嘈杂数据中包含的复杂实体的 NER 鲁棒性的未来研究需要受到关注。