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.

本研究提出了一种基于触发器的图神经网络（Trigger-GNN），通过实体触发编码和语义匹配获取补充注释嵌入，并利用高效的图传递体系结构解决嵌套实体问题。实验表明，Trigger-GNN 在四个公共 NER 数据集上始终优于基线，并能有效缓解嵌套 NER。