Named entity recognition (NER) task aims at identifying entities from a piece
of text that belong to predefined semantic types such as person, location,
organization, etc. The state-of-the-art solutions for flat entities NER
commonly suffer from capturing the fine-grained semantic information in
underlying texts. The existing span-based approaches overcome this limitation,
but the computation time is still a concern. In this work, we propose a novel
span-based NER framework, namely Global Pointer (GP), that leverages the
relative positions through a multiplicative attention mechanism. The ultimate
goal is to enable a global view that considers the beginning and the end
positions to predict the entity. To this end, we design two modules to identify
the head and the tail of a given entity to enable the inconsistency between the
training and inference processes. Moreover, we introduce a novel classification
loss function to address the imbalance label problem. In terms of parameters,
we introduce a simple but effective approximate method to reduce the training
parameters. We extensively evaluate GP on various benchmark datasets. Our
extensive experiments demonstrate that GP can outperform the existing solution.
Moreover, the experimental results show the efficacy of the introduced loss
function compared to softmax and entropy alternatives.

本文提出了一种基于跨度的命名实体识别框架 ——Global Pointer（GP），通过引入一种乘性注意机制来利用相对位置关系，以便在全局范围内考虑开始和结束位置来预测命名实体，并且利用两个模块来组合实体头和实体尾以解决训练过程和推理过程之间的不一致性，同时引入新的分类损失函数来解决不平衡标签问题，大量实验证明，GP 比现有解决方案表现更好。