Nested named entity recognition (NER) has been receiving increasing
attention. Recently, (Fu et al, 2021) adapt a span-based constituency parser to
tackle nested NER. They treat nested entities as partially-observed
constituency trees and propose the masked inside algorithm for partial
marginalization. However, their method cannot leverage entity heads, which have
been shown useful in entity mention detection and entity typing. In this work,
we resort to more expressive structures, lexicalized constituency trees in
which constituents are annotated by headwords, to model nested entities. We
leverage the Eisner-Satta algorithm to perform partial marginalization and
inference efficiently. In addition, we propose to use (1) a two-stage strategy
(2) a head regularization loss and (3) a head-aware labeling loss in order to
enhance the performance. We make a thorough ablation study to investigate the
functionality of each component. Experimentally, our method achieves the
state-of-the-art performance on ACE2004, ACE2005 and NNE, and competitive
performance on GENIA, and meanwhile has a fast inference speed.

本文提出使用词性识别和命名实体识别相结合的方法，在 NER 中推广了语法结构树，并引入局部边际化算法来解决名字嵌套的问题。实验结果表明，使用本文提出的方法在 ACE2004，ACE2005 和 NNE 数据集上达到了现有最优效果，并在 GENIA 数据集上表现出色，并且具有快速的推理速度。

作为潜在的词汇化组成句子分析的嵌套命名实体识别

Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing

In Natural Language Processing (NLP), we often need to extract information
from tree topology. Sentence structure can be represented via a dependency tree
or a constituency tree structure. For this reason, a variant of LSTMs, named
Tree-LSTM, was proposed to work on tree topology. In this paper, we design a
generalized attention framework for both dependency and constituency trees by
encoding variants of decomposable attention inside a Tree-LSTM cell. We
evaluated our models on a semantic relatedness task and achieved notable
results compared to Tree-LSTM based methods with no attention as well as other
neural and non-neural methods and good results compared to Tree-LSTM based
methods with attention.

通过在 Tree-LSTM 单元中引入可分解注意力的变体，我们设计了一种广义的注意力框架，适用于依赖树和组成树结构，并在语义相关性任务中评估了模型，与不使用注意力的 Tree-LSTM 方法以及其他神经和非神经方法相比表现显著。