Sequential labeling-based NER approaches restrict each word belonging to at
most one entity mention, which will face a serious problem when recognizing
nested entity mentions. In this paper, we propose to resolve this problem by
modeling and leveraging the head-driven phrase structures of entity mentions,
i.e., although a mention can nest other mentions, they will not share the same
head word. Specifically, we propose Anchor-Region Networks (ARNs), a
sequence-to-nuggets architecture for nested mention detection. ARNs first
identify anchor words (i.e., possible head words) of all mentions, and then
recognize the mention boundaries for each anchor word by exploiting regular
phrase structures. Furthermore, we also design Bag Loss, an objective function
which can train ARNs in an end-to-end manner without using any anchor word
annotation. Experiments show that ARNs achieve the state-of-the-art performance
on three standard nested entity mention detection benchmarks.

本文提出了一种解决嵌套实体识别问题的序列到区块的模型，即 Anchor-Region Networks，使用此模型可以达到状态 - of-the-art 水平，并且不需要使用任何锚词标注。

通过锚点 - 区域网络进行嵌套实体提取的序列到片段转换

Sequence-to-Nuggets: Nested Entity Mention Detection via Anchor-Region  Networks

Named entity recognition (NER) is widely used in natural language processing
applications and downstream tasks. However, most NER tools target flat
annotation from popular datasets, eschewing the semantic information available
in nested entity mentions. We describe NNE---a fine-grained, nested named
entity dataset over the full Wall Street Journal portion of the Penn Treebank
(PTB). Our annotation comprises 279,795 mentions of 114 entity types with up to
6 layers of nesting. We hope the public release of this large dataset for
English newswire will encourage development of new techniques for nested NER.

本文介绍了 NNE 数据集，这是一种精细的、嵌套的命名实体识别数据集，可应用于英语新闻文本中。该数据集包含多个命名实体类型、多层嵌套，希望其公开发布能推动嵌套 NER 新技术的发展。