  So far, discontinuous named entity recognition (NER) has received increasing
research attention and many related methods have surged such as
hypergraph-based methods, span-based methods, and sequence-to-sequence
(Seq2Seq) methods, etc. However, these methods more or less suffer from some
problems such as decoding ambiguity and efficiency, which limit their
performance. Recently, grid-tagging methods, which benefit from the flexible
design of tagging systems and model architectures, have shown superiority to
adapt for various information extraction tasks. In this paper, we follow the
line of such methods and propose a competitive grid-tagging model for
discontinuous NER. We call our model TOE because we incorporate two kinds of
Tag-Oriented Enhancement mechanisms into a state-of-the-art (SOTA) grid-tagging
model that casts the NER problem into word-word relationship prediction. First,
we design a Tag Representation Embedding Module (TREM) to force our model to
consider not only word-word relationships but also word-tag and tag-tag
relationships. Concretely, we construct tag representations and embed them into
TREM, so that TREM can treat tag and word representations as
queries/keys/values and utilize self-attention to model their relationships. On
the other hand, motivated by the Next-Neighboring-Word (NNW) and Tail-Head-Word
(THW) tags in the SOTA model, we add two new symmetric tags, namely
Previous-Neighboring-Word (PNW) and Head-Tail-Word (HTW), to model more
fine-grained word-word relationships and alleviate error propagation from tag
prediction. In the experiments of three benchmark datasets, namely CADEC,
ShARe13 and ShARe14, our TOE model pushes the SOTA results by about 0.83%,
0.05% and 0.66% in F1, demonstrating its effectiveness.
