The task of named entity recognition (NER) is normally divided into nested
NER and flat NER depending on whether named entities are nested or not. Models
are usually separately developed for the two tasks, since sequence labeling
models, the most widely used backbone for flat NER, are only able to assign a
single label to a particular token, which is unsuitable for nested NER where a
token may be assigned several labels.
In this paper, we propose a unified framework that is capable of handling
both flat and nested NER tasks. Instead of treating the task of NER as a
sequence labeling problem, we propose to formulate it as a machine reading
comprehension (MRC) task. For example, extracting entities with the
\textsc{per} label is formalized as extracting answer spans to the question
"{\it which person is mentioned in the text?}". This formulation naturally
tackles the entity overlapping issue in nested NER: the extraction of two
overlapping entities for different categories requires answering two
independent questions. Additionally, since the query encodes informative prior
knowledge, this strategy facilitates the process of entity extraction, leading
to better performances for not only nested NER, but flat NER.
We conduct experiments on both {\em nested} and {\em flat} NER datasets.
Experimental results demonstrate the effectiveness of the proposed formulation.
We are able to achieve vast amount of performance boost over current SOTA
models on nested NER datasets, i.e., +1.28, +2.55, +5.44, +6.37, respectively
on ACE04, ACE05, GENIA and KBP17, along with SOTA results on flat NER datasets,
i.e.,+0.24, +1.95, +0.21, +1.49 respectively on English CoNLL 2003, English
OntoNotes 5.0, Chinese MSRA, Chinese OntoNotes 4.0.

本文提出一种统一的框架，通过将命名实体识别任务形式化为机器阅读理解问题，能够同时处理平面 NER 和嵌套 NER 任务，并在实验中验证该框架在嵌套 NER 数据集上大幅提高性能，并在平面 NER 中获得当前最佳结果。