We introduce Universal NER (UNER), an open, community-driven project to develop gold-standard NER benchmarks in many languages. The overarching goal of UNER is to provide high-quality, cross-lingually consistent annotations to facilitate and standardize multilingual NER research. UNER v1 contains 18 datasets annotated with named entities in a cross-lingual consistent schema across 12 diverse languages. In this paper, we detail the dataset creation and composition of UNER; we also provide initial modeling baselines on both in-language and cross-lingual learning settings. We release the data, code, and fitted models to the public.

介绍了一个名为Universal NER (UNER)的开源项目，旨在开发多种语言的黄金标准NER基准；UNER的目标是提供高质量的、跨语言一致的标注，以促进和标准化多语言NER研究。UNER v1包含使用一致的跨语言模式在12种不同语言中注释的18个数据集。在本文中，我们详细介绍了UNER的数据集创建和组成，并提供了在本语言和跨语言学习设置下的初始建模基线。我们向公众发布了数据、代码和拟合模型。

通用NER：金标准多语言命名实体识别基准