  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.

Computation and Language;Artificial Intelligence