  In this paper, we propose a new strategy for the task of named entity
recognition (NER). We cast the task as a query-based machine reading
comprehension task: e.g., the task of extracting entities with PER is
formalized as answering the question of "which person is mentioned in the text
?". Such a strategy comes with the advantage that it solves the long-standing
issue of handling overlapping or nested entities (the same token that
participates in more than one entity categories) with sequence-labeling
techniques for NER. Additionally, since the query encodes informative prior
knowledge, this strategy facilitates the process of entity extraction, leading
to better performances. We experiment the proposed model on five widely used
NER datasets on English and Chinese, including MSRA, Resume, OntoNotes, ACE04
and ACE05. The proposed model sets new SOTA results on all of these datasets.
