The Journal of China Universities of Posts and Telecommunications ›› 2023, Vol. 30 ›› Issue (5): 32-41.doi: 10. 19682 / j. cnki. 1005-8885. 2023. 0011

Special Issue: Special Topic on Digital Human

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Pointer-prototype fusion network for few-shot named entity recognition

  Hai-Ying ZHAO2 , Xuan GUO1   

  1. 1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China  2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2023-07-20 Revised:2023-09-02 Online:2023-10-31 Published:2023-10-30
  • Contact: Hai-Ying ZHAO
  • Supported by:
    the National Key Research and Development Project (2021YFF0901701).


   Few-shot named entity recognition (NER) aims to identify named entities in new domains using a limited amount of annotated data. Previous methods divided this task into entity span detection and entity classification, achieving good results. However these methods are limited by the imbalance between the entity and non-entity categories due to the use of sequence labeling for entity span detection. To this end, a point-proto network ( PPN) combining pointer and prototypical networks was proposed. Specifically, the pointer network generates the position of entities in sentences in the entity span detection stage. The prototypical network builds semantic prototypes of entity types and classifies entities based on their distance from these prototypes in the entity classification stage. Moreover, the low-rank adaptation ( LoRA) fine-tuning method, which involves freezing the pre-trained weights and injecting a trainable decomposition matrix, reduces the parameters that need to be trained and saved. Extensive experiments on the few-shot NER Dataset (Few-NERD) and Cross-Dataset demonstrate the superiority of PPN in this domain.

Key words:

few-shot named entity recognition (NER), pointer network, prototypical network, low-rank adaptation