Named entity recognition (NER) is a well-studied task in natural language
processing. Traditional NER research only deals with flat entities and ignores
nested entities. The span-based methods treat entity recognition as a span
classification task. Although these methods have the innate ability to handle
nested NER, they suffer from high computational cost, ignorance of boundary
information, under-utilization of the spans that partially match with entities,
and difficulties in long entity recognition. To tackle these issues, we propose
a two-stage entity identifier. First we generate span proposals by filtering
and boundary regression on the seed spans to locate the entities, and then
label the boundary-adjusted span proposals with the corresponding categories.
Our method effectively utilizes the boundary information of entities and
partially matched spans during training. Through boundary regression, entities
of any length can be covered theoretically, which improves the ability to
recognize long entities. In addition, many low-quality seed spans are filtered
out in the first stage, which reduces the time complexity of inference.
Experiments on nested NER datasets demonstrate that our proposed method
outperforms previous state-of-the-art models.

该研究提出两阶段标识器以识别嵌套实体，其通过过滤种子跨度和边界回归生成跨度建议，并准确地标记跨度与相应类别。实验结果表明，该方法优于以往最先进的模型。