Gaussian Prior Reinforcement Learning for Nested Named Entity
  Recognition
Yawen Yang Xuming Hu Fukun Ma Shu'ang Li Aiwei Liu Lijie Wen Philip S. Yu
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.