Current state-of-the-art approaches for named entity recognition (NER) typically consider text at the sentence-level and thus do not model information that crosses sentence boundaries. However, the use of transformer-based models for NER offers natural options for capturing document-level features. In this paper, we perform a comparative evaluation of document-level features in the two standard NER architectures commonly considered in the literature, namely "fine-tuning" and "feature-based LSTM-CRF". We evaluate different hyperparameters for document-level features such as context window size and enforcing document-locality. We present experiments from which we derive recommendations for how to model document context and present new state-of-the-art scores on several CoNLL-03 benchmark datasets. Our approach is integrated into the Flair framework to facilitate reproduction of our experiments.

翻译：目前用于名称实体识别(NER)的最新方法通常在句级上考虑文本,因此不作为跨句界信息的模型。然而,基于变压器的NER模型的使用为捕捉文件级特征提供了自然的选择。在本文件中,我们对文献中通常考虑的两种标准NER结构,即“微调”和“基于功能的LSTM-CRF”中的文档级特征进行了比较性评估。我们评估了环境窗口大小和执行文件位置等文件级特征的不同超参数。我们介绍了一些实验,从中我们就如何模拟文件背景提出建议,并就若干CONLLL-03基准数据集提出最新评分。我们的方法被纳入了Flair框架,以便利复制我们的实验。