We present OpenNER 1.0, a standardized collection of openly-available named entity recognition (NER) datasets. OpenNER contains 36 NER corpora that span 52 languages, human-annotated in varying named entity ontologies. We correct annotation format issues, standardize the original datasets into a uniform representation with consistent entity type names across corpora, and provide the collection in a structure that enables research in multilingual and multi-ontology NER. We provide baseline results using three pretrained multilingual language models and two large language models to compare the performance of recent models and facilitate future research in NER. We find that no single model is best in all languages and that significant work remains to obtain high performance from LLMs on the NER task. OpenNER is released at https://github.com/bltlab/open-ner.

翻译：我们推出OpenNER 1.0，这是一个标准化的、公开可用的命名实体识别（NER）数据集集合。OpenNER包含36个NER语料库，涵盖52种语言，这些语料库采用不同命名实体本体进行人工标注。我们修正了标注格式问题，将原始数据集标准化为统一表示形式，确保跨语料库的实体类型名称一致，并以支持多语言和多本体NER研究的结构提供该集合。我们使用三种预训练多语言模型和两种大语言模型提供了基线结果，以比较近期模型的性能，并促进NER领域的未来研究。我们发现，没有单一模型在所有语言中表现最佳，且要让大语言模型在NER任务上实现高性能仍需大量工作。OpenNER发布于https://github.com/bltlab/open-ner。