全局与局部上下文在命名实体识别中的作用
The Role of Global and Local Context in Named Entity Recognition
AI总结：
针对预训练Transformer模型处理长文档时仅用局部上下文、可能影响命名实体识别性能的问题，本文探究全局文档上下文的作用及与局部上下文的关系，发现正确检索全局上下文对性能提升作用更大，为相关后续研究提供方向。
摘要：
基于Transformer的预训练模型近期在命名实体识别（NER）任务中表现出色。由于其自注意力机制的复杂性导致无法一次性处理长文档，这类模型通常以顺序方式应用。遗憾的是，这种方法仅融入了局部上下文，无法利用小说等长文档中的全局文档上下文，可能会影响性能。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。我们发现，正确检索全局文档上下文对性能的影响比仅利用局部上下文更大，这为进一步研究如何更好地检索该上下文提供了动力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
