Neural named entity recognition (NER) models may easily encounter the
over-confidence issue, which degrades the performance and calibration. Inspired
by label smoothing and driven by the ambiguity of boundary annotation in NER
engineering, we propose boundary smoothing as a regularization technique for
span-based neural NER models. It re-assigns entity probabilities from annotated
spans to the surrounding ones. Built on a simple but strong baseline, our model
achieves results better than or competitive with previous state-of-the-art
systems on eight well-known NER benchmarks. Further empirical analysis suggests
that boundary smoothing effectively mitigates over-confidence, improves model
calibration, and brings flatter neural minima and more smoothed loss
landscapes.

通过在命名实体识别中引入边界平滑正则化技术，将命名实体的概率分布从注释的跨度重新分配到周围的跨度，从而有效缓解了过度自信问题，改善了模型的校准性并获得更好的结果。

命名实体识别的边界平滑

Boundary Smoothing for Named Entity Recognition

This study improves the performance of neural named entity recognition by a
margin of up to 11% in F-score on the example of a low-resource language like
German, thereby outperforming existing baselines and establishing a new
state-of-the-art on each single open-source dataset. Rather than designing
deeper and wider hybrid neural architectures, we gather all available resources
and perform a detailed optimization and grammar-dependent morphological
processing consisting of lemmatization and part-of-speech tagging prior to
exposing the raw data to any training process. We test our approach in a
threefold monolingual experimental setup of a) single, b) joint, and c)
optimized training and shed light on the dependency of downstream-tasks on the
size of corpora used to compute word embeddings.

该研究针对德语等低资源语言，通过优化和语法相关的形态处理来改进神经命名实体识别的性能，提高了 F1 分数高达 11％的水平，并在每个开放数据集上建立了一种新的最先进的基准线。