Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 296-304.DOI: 10.3778/j.issn.1673-9418.2107031

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Chinese Named Entity Recognition

ZHAO Shan, LUO Rui, CAI Zhiping+()   

  1. College of Computer, National University of Defense Technology, Changsha 410073, China
  • Received:2021-07-08 Revised:2021-09-22 Online:2022-02-01 Published:2021-09-28
  • About author:ZHAO Shan, born in 1990, Ph.D. candidate. His research interest is natural language processing.
    LUO Rui, born in 1998, M.S. candidate. His research interest is natural language processing.
    CAI Zhiping, born in 1975, professor, Ph.D. supervisor, distinguished member of CCF. His research interests include network security, big data and dispersed computing.
  • Supported by:
    National Key Research and Development Program of China(2020YFC2003400)


赵山, 罗睿, 蔡志平+()   

  1. 国防科技大学 计算机学院,长沙 410073
  • 通讯作者: + E-mail:
  • 作者简介:赵山(1990—),男,安徽六安人,博士研究生,主要研究方向为自然语言处理。
  • 基金资助:


The Chinese named entity recognition (NER) task is a sub-task within the information extraction domain, where the task goal is to find, identify and classify relevant entities, such as names of people, places and organizations, from sentences given a piece of unstructured text. Chinese named entity recognition is a fundamental task in the field of natural language processing (NLP) and plays an important role in many downstream NLP tasks, including information retrieval, relationship extraction and question and answer systems. This paper provides a comprehensive review of existing neural network-based word-character lattice structures for Chinese NER models. Firstly, this paper introduces that Chinese NER is more difficult than English NER, and there are difficulties and challenges such as difficulty in determining the boundaries of Chinese text-related entities and complex Chinese grammatical structures. Secondly, this paper investigates the most representative lattice-structured Chinese NER models under different neural network architectures (RNN (recurrent neural network), CNN (convolutional neural network), GNN (graph neural network) and Transformer). Since word sequence information can capture more boundary information for character-based sequence learning, in order to explicitly exploit the lexical information associated with each character, some prior work has proposed integrating word information into character sequences via word-character lattice structures. These neural network-based word-character lattice structures perform significantly better than word-based or character-based approaches on the Chinese NER task. Finally, this paper introduces the dataset and evaluation criteria of Chinese NER.

Key words: named entity recognition (NER), lattice structure, neural network



关键词: 命名实体识别(NER), 晶格结构, 神经网络

CLC Number: