For Named Entity Recognition (NER), sequence labeling-based and span-based
paradigms are quite different. Previous research has demonstrated that the two
paradigms have clear complementary advantages, but few models have attempted to
leverage these advantages in a single NER model as far as we know. In our
previous work, we proposed a paradigm known as Bundling Learning (BL) to
address the above problem. The BL paradigm bundles the two NER paradigms,
enabling NER models to jointly tune their parameters by weighted summing each
paradigm's training loss. However, three critical issues remain unresolved:
When does BL work? Why does BL work? Can BL enhance the existing
state-of-the-art (SOTA) NER models? To address the first two issues, we
implement three NER models, involving a sequence labeling-based model--SeqNER,
a span-based NER model--SpanNER, and BL-NER that bundles SeqNER and SpanNER
together. We draw two conclusions regarding the two issues based on the
experimental results on eleven NER datasets from five domains. We then apply BL
to five existing SOTA NER models to investigate the third issue, consisting of
three sequence labeling-based models and two span-based models. Experimental
results indicate that BL consistently enhances their performance, suggesting
that it is possible to construct a new SOTA NER system by incorporating BL into
the current SOTA system. Moreover, we find that BL reduces both entity boundary
and type prediction errors. In addition, we compare two commonly used labeling
tagging methods as well as three types of span semantic representations.

本研究通过绑定学习（BL）范例将序列标注和范围标注两种命名实体识别（NER）范例结合在一起，以解决此前不能同时利用两种范例优点的问题，并发现 BL 可显著提升 NER 模型的性能。

Win-Win 合作：捆绑序列和跨度模型进行命名实体识别

Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition

Recently, span-based constituency parsing has achieved competitive accuracies
with extremely simple models by using bidirectional RNNs to model "spans".
However, the minimal span parser of Stern et al (2017a) which holds the current
state of the art accuracy is a chart parser running in cubic time, $O(n^3)$,
which is too slow for longer sentences and for applications beyond sentence
boundaries such as end-to-end discourse parsing and joint sentence boundary
detection and parsing. We propose a linear-time constituency parser with RNNs
and dynamic programming using graph-structured stack and beam search, which
runs in time $O(n b^2)$ where $b$ is the beam size. We further speed this up to
$O(n b\log b)$ by integrating cube pruning. Compared with chart parsing
baselines, this linear-time parser is substantially faster for long sentences
on the Penn Treebank and orders of magnitude faster for discourse parsing, and
achieves the highest F1 accuracy on the Penn Treebank among single model
end-to-end systems.

提出了一种基于 RNNs 和动态规划的图结构堆叠和波束搜索的线性时间组成成分句法分析器，通过集成立方剪枝，可以将其运行速度进一步提高到 O（n b log b），相对于图表解析基线，此线性时间解析器在长句子上的速度明显更快，对于话语分析来说速度更快，是单模型端到端系统中在 Penn Treebank 上达到的最高 F1 准确度。