In this work, we address the NER problem by splitting it into two logical
sub-tasks: (1) Span Detection which simply extracts entity mention spans
irrespective of entity type; (2) Span Classification which classifies the spans
into their entity types. Further, we formulate both sub-tasks as
question-answering (QA) problems and produce two leaner models which can be
optimized separately for each sub-task. Experiments with four cross-domain
datasets demonstrate that this two-step approach is both effective and time
efficient. Our system, SplitNER outperforms baselines on OntoNotes5.0, WNUT17
and a cybersecurity dataset and gives on-par performance on BioNLP13CG. In all
cases, it achieves a significant reduction in training time compared to its QA
baseline counterpart. The effectiveness of our system stems from fine-tuning
the BERT model twice, separately for span detection and classification. The
source code can be found at this https URL

我们通过将 NER 问题分成两个逻辑子任务来解决，即 Span Detection 和 Span Classification，进一步将两个子任务形式化为问答问题，使用两个较为简洁的模型针对每个子任务进行优化。与 OntoNotes5.0、WNUT17 和一个网络安全数据集的基准模型相比，我们的系统 SplitNER 表现出色，并且在 BioNLP13CG 数据集上取得了相当的性能，同时与 QA 基准模型相比，训练时间显著减少。我们的系统的有效性来自对 BERT 模型进行两次微调，分别用于 span detection 和 classification。该研究代码可在给定的 https URL 上找到。

Split-NER：基于两个问题 - 回答分类的命名实体识别

Split-NER: Named Entity Recognition via Two Question-Answering-based  Classifications

Nested entities are observed in many domains due to their compositionality,
which cannot be easily recognized by the widely-used sequence labeling
framework. A natural solution is to treat the task as a span classification
problem. To learn better span representation and increase classification
performance, it is crucial to effectively integrate heterogeneous factors
including inside tokens, boundaries, labels, and related spans which could be
contributing to nested entities recognition. To fuse these heterogeneous
factors, we propose a novel triaffine mechanism including triaffine attention
and scoring. Triaffine attention uses boundaries and labels as queries and uses
inside tokens and related spans as keys and values for span representations.
Triaffine scoring interacts with boundaries and span representations for
classification. Experiments show that our proposed method outperforms previous
span-based methods, achieves the state-of-the-art $F_1$ scores on nested NER
datasets GENIA and KBP2017, and shows comparable results on ACE2004 and
ACE2005.

使用 triaffine 机制来完成嵌套实体识别，包括三元注意力和打分机制，实验结果表明，该方法在嵌套命名实体识别任务中取得了最优结果。