Named entity recognition (NER) remains challenging when entity mentions can
be discontinuous. Existing methods break the recognition process into several
sequential steps. In training, they predict conditioned on the golden
intermediate results, while at inference relying on the model output of the
previous steps, which introduces exposure bias. To solve this problem, we first
construct a segment graph for each sentence, in which each node denotes a
segment (a continuous entity on its own, or a part of discontinuous entities),
and an edge links two nodes that belong to the same entity. The nodes and edges
can be generated respectively in one stage with a grid tagging scheme and
learned jointly using a novel architecture named Mac. Then discontinuous NER
can be reformulated as a non-parametric process of discovering maximal cliques
in the graph and concatenating the spans in each clique. Experiments on three
benchmarks show that our method outperforms the state-of-the-art (SOTA)
results, with up to 3.5 percentage points improvement on F1, and achieves 5x
speedup over the SOTA model.

本文提出了一种基于 Mac 模型的分段图算法，用于解决实体识别中异构实体的问题。该算法可将异构实体视作图中的节点，通过识别图中的最大团并连接团内的连续实体段，实现对异构实体的非参数化处理，在三个基准数据集上的结果证明该方法领先于目前的最优算法，并且速度提高了 5 倍。