There has been a growing academic interest in the recognition of nested named
entities in many domains. We tackle the task with a novel local
hypergraph-based method: We first propose start token candidates and generate
corresponding queries with their surrounding context, then use a query-based
sequence labeling module to form a local hypergraph for each candidate. An end
token estimator is used to correct the hypergraphs and get the final
predictions. Compared to span-based approaches, our method is free of the high
computation cost of span sampling and the risk of losing long entities.
Sequential prediction makes it easier to leverage information in word order
inside nested structures, and richer representations are built with a local
hypergraph. Experiments show that our proposed method outperforms all the
previous hypergraph-based and sequence labeling approaches with large margins
on all four nested datasets. It achieves a new state-of-the-art F1 score on the
ACE 2004 dataset and competitive F1 scores with previous state-of-the-art
methods on three other nested NER datasets: ACE 2005, GENIA, and KBP 2017.

提出了一种基于本地超图的方法识别嵌套命名实体，使用查询构造本地超图，通过序列标记模块实现嵌套结构内部信息的可利用性，综合四个数据集的表现表明，该方法优于之前的方法，并在 ACE 2004 数据集上实现了新的最佳表现。