Nested named entity recognition (NER) aims to identify the entity boundaries and recognize categories of the named entities in a complex hierarchical sentence. Some works have been done using character-level, word-level, or lexicon-level based models. However, such researches ignore the role of the complementary annotations. In this paper, we propose a trigger-based graph neural network (Trigger-GNN) to leverage the nested NER. It obtains the complementary annotation embeddings through entity trigger encoding and semantic matching, and tackle nested entity utilizing an efficient graph message passing architecture, aggregation-update mode. We posit that using entity triggers as external annotations can add in complementary supervision signals on the whole sentences. It helps the model to learn and generalize more efficiently and cost-effectively. Experiments show that the Trigger-GNN consistently outperforms the baselines on four public NER datasets, and it can effectively alleviate the nested NER.

翻译：内列名称实体识别(NER)的目的是在复杂的等级句子中确定实体边界和识别被点名实体的类别,有些工作是使用基于字符级、字级或词汇级的模型完成的,但这类研究忽略了补充说明的作用。在本文件中,我们提议了一个基于触发的图形神经网络(Trigger-GNNN)来利用嵌巢式 NER 。它通过实体触发编码和语义匹配获得补充说明嵌入,并利用高效的图形信息传递结构、汇总更新模式处理嵌巢实体。我们假设使用实体作为外部说明可以在整个句子的补充监督信号中添加。它有助于模型学习和普及更有效率和成本效益更高的信息。实验表明,Trigger-GNNN在四个公共网络数据集上始终超越基线,它能够有效地缓解嵌式NER。