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。