Survey of Chinese Named Entity Recognition Research

被引：0
作者：
Zhao, Jigui [1 ,2 ,3 ]
Qian, Yurong [1 ,2 ,3 ]
Wang, Kui [4 ]
Hou, Shuxiang [2 ,3 ,5 ]
Chen, Jiaying [1 ,2 ,3 ]
机构：
[1] School of Software, Xinjiang University, Urumqi
[2] Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Urumqi
[3] Key Laboratory of Software Engineering, Xinjiang University, Urumqi
[4] School of Economics and Management, University of the Chinese Academy of Sciences, Beijing
[5] School of Information Science and Engineering, Xinjiang University, Urumqi
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 01期
关键词：
Chinese named entity recognition (CNER); deep learning; machine learning; natural language processing; pre-training models;
D O I：
10.3778/j.issn.1002-8331.2304-0398
中图分类号：
学科分类号：
摘要：
Named entity recognition (NER) is one of the most fundamental tasks in natural language processing, and its main content is to identify the entity types and boundaries with specific meanings in natural language text. However, the data samples of Chinese named entity recognition (CNER) have problems such as blurred word boundaries, semantic diversity, blurred morphological features and small Chinese corpus content, which make it difficult to improve the performance of Chinese NER. In this paper, firstly, the dataset, annotation scheme and evaluation index of CNER are introduced. Secondly, according to the research process of CNER, CNER methods are classified into three categories: rule-based methods, statistical-based methods and deep learning-based methods, and the main models of CNER based on deep learning in the past five years are summarized. Finally, the research trends of CNER are discussed to provide some reference for the proposal of new methods and future research directions. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
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页码：15 / 27
页数：12
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