结合原型网络的远程监督命名实体识别方法

Aiming at the problem of entity category labeling errors in the process of using distant supervision to label text entities, it is difficult for the model to effectively distinguish the category characteristics of each entity and affect the accuracy of the model. A named entity recognition （NER）method was proposed in this paper. It was designed to use pre-trained prototypical network coding to correctly label entities to generate category prototype representations, and to filter those far away samples from category prototypes in the corpus. Experiments show that the use of the prototype network can effectively improve the annotation quality of the corpus and improve the performance of the model.