Research progress in Chinese named entity recognition in the financial field

doi:10.3969/j.issn.1000-5641.2021.05.001

Abstract

Abstract:

As one of the basic components of natural language processing, named entity recognition (NER) has been an active area of research both domestically in China and abroad. With the rapid development of financial applications, Chinese NER has improved over time and been applied successfully throughout the financial industry. This paper provides a summary of the current state of research and future development trends for Chinese NER methods in the financial field. Firstly, the paper introduces concepts related to NER and the characteristics of Chinese NER in the financial field. Then, based on the development process, the paper provides an overview of detailed characteristics and typical models for dictionary and rule-based methods, statistical machine learning-based methods, and deep learning-based methods. Next, the paper summarizes public data collection tools, evaluation methods, and applications of Chinese NER in the financial industry. Finally, the paper explores current challenges and future development trends.

Key words: natural language processing, Chinese named entity recognition, financial field

CLC Number:

TP399

Qiurong XU, Peng ZHU, Yifeng LUO, Qiwen DONG. Research progress in Chinese named entity recognition in the financial field[J]. Journal of East China Normal University(Natural Science), 2021, 2021(5): 1-13.

Figures/Tables 5

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字符	BIO	BIOES	BMES
B-begin	实体开头	实体开头	实体开头
I-inside	实体中间	实体中间	实体中间
O-outside	非实体	非实体
M-middle			实体结尾
E-end		实体结尾	实体结尾
S-single		单个字符即实体	单个字符

真实值	预测值
真实值	Positive	Negative
Positive	True Positive(TP, 真正例)	False Negative(FN, 假负例)
Negative	False Positive(FP, 假正例)	True Negative(TN, 真负例)

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