Named Entity Recognition (NER) is a well and widely studied task in natural
language processing. Recently, the nested NER has attracted more attention
since its practicality and difficulty. Existing works for nested NER ignore the
recognition order and boundary position relation of nested entities. To address
these issues, we propose a novel seq2seq model named GPRL, which formulates the
nested NER task as an entity triplet sequence generation process. GPRL adopts
the reinforcement learning method to generate entity triplets decoupling the
entity order in gold labels and expects to learn a reasonable recognition order
of entities via trial and error. Based on statistics of boundary distance for
nested entities, GPRL designs a Gaussian prior to represent the boundary
distance distribution between nested entities and adjust the output probability
distribution of nested boundary tokens. Experiments on three nested NER
datasets demonstrate that GPRL outperforms previous nested NER models.

提出了一种新颖的基于 seq2seq 模型与强化学习方法的实体序列生成模型 GPRL，并利用高斯先验表示嵌套实体边界之间的距离分布，调整输出概率分布并能合理地识别嵌套实体，实验结果显示 GPRL 在三个嵌套实体识别数据集上表现优异。