Nested named entity recognition (nested NER) is a fundamental task in natural
language processing. Various span-based methods have been proposed to detect
nested entities with span representations. However, span-based methods do not
consider the relationship between a span and other entities or phrases, which
is helpful in the NER task. Besides, span-based methods have trouble predicting
long entities due to limited span enumeration length. To mitigate these issues,
we present the Propose-and-Refine Network (PnRNet), a two-stage set prediction
network for nested NER. In the propose stage, we use a span-based predictor to
generate some coarse entity predictions as entity proposals. In the refine
stage, proposals interact with each other, and richer contextual information is
incorporated into the proposal representations. The refined proposal
representations are used to re-predict entity boundaries and classes. In this
way, errors in coarse proposals can be eliminated, and the boundary prediction
is no longer constrained by the span enumeration length limitation.
Additionally, we build multi-scale sentence representations, which better model
the hierarchical structure of sentences and provide richer contextual
information than token-level representations. Experiments show that PnRNet
achieves state-of-the-art performance on four nested NER datasets and one flat
NER dataset.

本文提出了基于 Propose-and-Refine 网络的多层次命名实体识别方法，通过预测基于语块提出的实体预测与交互来减少粗糙提议的误差，同时使用多层次句子表示方法提高语境信息丰富度，在四个嵌套 NER 数据集和一个平面 NER 数据集上表现出卓越的性能。