Nowadays, many Natural Language Processing (NLP) tasks see the demand for incorporating knowledge external to the local information to further improve the performance. However, there is little related work on Named Entity Recognition (NER), which is one of the foundations of NLP. Specifically, no studies were conducted on the query generation and re-ranking for retrieving the related information for the purpose of improving NER. This work demonstrates the effectiveness of a DNN-based query generation method and a mention-aware re-ranking architecture based on BERTScore particularly for NER. In the end, a state-of-the-art performance of 61.56 micro-f1 score on WNUT17 dataset is achieved.

翻译：如今，许多自然语言处理任务都要求融入局部信息以外的外部知识以进一步提升性能。然而，作为自然语言处理基础之一的命名实体识别，相关研究却十分有限。具体而言，尚未有研究探讨如何通过查询生成与重排序来检索相关信息以改进命名实体识别。本文验证了基于深度神经网络的查询生成方法及基于BERTScore的提及感知重排序架构在命名实体识别中的有效性。最终，在WNUT17数据集上取得了61.56微平均F1分数的当前最优性能。