端到端的嵌套命名实体识别方法研究

Named entity recognition(NER)is regarded as a basic research in natural language processing. Inspired by the one-stage object detection algorithm in computer vision, this paper proposes an effective end-to-end method for identifying nested named entities by using its algorithm idea and introducing regression operation. Based on multi- task learning framework, the paper uses deep neural network to transform sentences into text feature graphs to regress nested entity boundaries, and designs centrality method to suppress low-quality boundaries. A comparative experiment is carried out with several methods on ACE2005 Chinese dataset. Experiments show that this method is effective in identifying nested named entities in text, and the computer vision algorithm idea and boundary regression mechanism achieve ideal results in natural language processing tasks.