Named entity recognition (NER) is the task to detect and classify the entity
spans in the text. When entity spans overlap between each other, this problem
is named as nested NER. Span-based methods have been widely used to tackle the
nested NER. Most of these methods will get a score $n \times n$ matrix, where
$n$ means the length of sentence, and each entry corresponds to a span.
However, previous work ignores spatial relations in the score matrix. In this
paper, we propose using Convolutional Neural Network (CNN) to model these
spatial relations in the score matrix. Despite being simple, experiments in
three commonly used nested NER datasets show that our model surpasses several
recently proposed methods with the same pre-trained encoders. Further analysis
shows that using CNN can help the model find more nested entities. Besides, we
found that different papers used different sentence tokenizations for the three
nested NER datasets, which will influence the comparison. Thus, we release a
pre-processing script to facilitate future comparison.

本文提出使用卷积神经网络来建模得分矩阵中的空间关系以解决嵌套的命名实体识别问题，实验证明相较于最近提出的同类方法，本文提出的方法更为优秀，并且发现不同的论文使用不同的句子标记化会对结果产生影响，因此提供一种易于使用的预处理脚本以便于日后比较。