So far, named entity recognition (NER) has been involved with three major
types, including flat, overlapped (aka. nested), and discontinuous NER, which
have mostly been studied individually. Recently, a growing interest has been
built for unified NER, tackling the above three jobs concurrently with one
single model. Current best-performing methods mainly include span-based and
sequence-to-sequence models, where unfortunately the former merely focus on
boundary identification and the latter may suffer from exposure bias. In this
work, we present a novel alternative by modeling the unified NER as word-word
relation classification, namely W^2NER. The architecture resolves the kernel
bottleneck of unified NER by effectively modeling the neighboring relations
between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-*
(THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in
which the unified NER is modeled as a 2D grid of word pairs. We then propose
multi-granularity 2D convolutions for better refining the grid representations.
Finally, a co-predictor is used to sufficiently reason the word-word relations.
We perform extensive experiments on 14 widely-used benchmark datasets for flat,
overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our
model beats all the current top-performing baselines, pushing the
state-of-the-art performances of unified NER.

利用 W^2NER 架构和 2D 卷积等方法，将命名实体识别 (NER) 统一为一个二维网格，通过分类识别实体词之间的关系，取得在 14 个基准数据集上显著的改进。