Many recent named entity recognition (NER) studies criticize flat NER for its
non-overlapping assumption, and switch to investigating nested NER. However,
existing nested NER models heavily rely on training data annotated with nested
entities, while labeling such data is costly. This study proposes a new
subtask, nested-from-flat NER, which corresponds to a realistic application
scenario: given data annotated with flat entities only, one may still desire
the trained model capable of recognizing nested entities. To address this task,
we train span-based models and deliberately ignore the spans nested inside
labeled entities, since these spans are possibly unlabeled entities. With
nested entities removed from the training data, our model achieves 54.8%, 54.2%
and 41.1% F1 scores on the subset of spans within entities on ACE 2004, ACE
2005 and GENIA, respectively. This suggests the effectiveness of our approach
and the feasibility of the task. In addition, the model's performance on flat
entities is entirely unaffected. We further manually annotate the nested
entities in the test set of CoNLL 2003, creating a nested-from-flat NER
benchmark. Analysis results show that the main challenges stem from the data
and annotation inconsistencies between the flat and nested entities.

该研究提出了一种名为 nested-from-flat NER 的新子任务，利用基于跨度的模型培训数据并有意忽略在已注释实体内的嵌套跨度，从而获得能够识别嵌套实体的训练模型，结果在多个数据集上得到了良好的成绩。