Named Entity Recognition (NER) is the task of identifying spans that
represent entities in sentences. Whether the entity spans are nested or
discontinuous, the NER task can be categorized into the flat NER, nested NER,
and discontinuous NER subtasks. These subtasks have been mainly solved by the
token-level sequence labelling or span-level classification. However, these
solutions can hardly tackle the three kinds of NER subtasks concurrently. To
that end, we propose to formulate the NER subtasks as an entity span sequence
generation task, which can be solved by a unified sequence-to-sequence
(Seq2Seq) framework. Based on our unified framework, we can leverage the
pre-trained Seq2Seq model to solve all three kinds of NER subtasks without the
special design of the tagging schema or ways to enumerate spans. We exploit
three types of entity representations to linearize entities into a sequence.
Our proposed framework is easy-to-implement and achieves state-of-the-art
(SoTA) or near SoTA performance on eight English NER datasets, including two
flat NER datasets, three nested NER datasets, and three discontinuous NER
datasets.

该研究提出了一种基于序列到序列的统一框架实现实体识别子任务的方法，既不需要特定的标记模式和枚举语法，也能够同时解决平面、嵌套和不连续型 NER 任务，通过使用三种实体表示方法，实现了出色的表现。