Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
This work addresses nested NER, a challenging problem in NLP, with incremental improvements over existing methods.
The paper tackles nested named entity recognition by proposing GPRL, a seq2seq model that uses reinforcement learning and a Gaussian prior to address recognition order and boundary position issues, achieving state-of-the-art performance on three datasets.
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.