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。