Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately only incorporates local context and prevents leveraging global document context in long documents such as novels, which might hinder performance. In this article, we explore the impact of global document context, and its relationships with local context. We find that correctly retrieving global document context has a greater impact on performance than only leveraging local context, prompting for further research on how to better retrieve that context.

翻译：基于Transformer的预训练模型在命名实体识别（NER）任务中近期展现出卓越性能。由于自注意力机制的复杂性限制了模型一次性处理长文档的能力，这类模型通常采用序列化方式应用。但这种处理方式仅能整合局部上下文，无法利用长篇文档（如小说）中的全局文档上下文，这可能会影响模型性能。本文探究了全局文档上下文的影响及其与局部上下文的关系。研究发现，正确检索全局文档上下文对性能的影响显著优于仅利用局部上下文，这为如何更有效地检索该上下文指明了进一步的研究方向。