Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities often are nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based approach based on joint labelling and semantic weighting of errors, evaluated on a collection of 19 th-century Paris trade directories. We evaluate approaches regarding the impact of supervised fine-tuning, unsupervised pre-training with noisy texts, and variation of IOB tagging formats. Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities. Even though all 3 approaches perform well in terms of F1 scores, joint labelling is most suitable for hierarchically structured data. Finally, our experiments reveal the superiority of the IO tagging format on such data.

翻译：命名实体识别是从数字化历史文档中创建结构化数据的关键步骤。传统命名实体识别方法处理扁平实体，而实体往往具有嵌套结构。例如，邮政地址可能包含街道名称和门牌号。本研究比较了三种嵌套命名实体识别方法，包括两种基于Transformer架构的最先进方法。我们提出了一种基于联合标注和语义加权误差的新型Transformer方法，并在19世纪巴黎贸易名录数据集上进行了评估。我们评估了监督微调、基于噪声文本的无监督预训练以及IOB标注格式变化对方法的影响。结果表明，尽管嵌套命名实体识别方法能够直接提取结构化数据，但它们在训练过程中并未从额外知识中获益，且在扁平实体上的性能与基础方法相当。虽然三种方法的F1分数均表现良好，但联合标注方法最适合层次结构化数据。最后，我们的实验揭示了IO标注格式在此类数据上的优越性。