金融领域中文命名实体识别研究进展

doi:10.3969/j.issn.1000-5641.2021.05.001

摘要/Abstract

摘要：

命名实体识别(Named Entity Recognition, NER)作为自然语言处理的基本任务之一, 一直以来都是国内外研究的热点. 随着金融互联网的快速发展, 迄今为止, 金融领域中文NER不断进步, 并得以应用到其他金融业务中. 为了方便研究者了解金融领域中文NER方法的发展状况和未来发展趋势, 进行了一项相关方法的研究和总结. 首先, 介绍了NER的相关概念和金融领域中文NER的特点; 然后, 按照金融领域中文NER的发展历程, 将研究方法分为基于字典和规则的方法、基于统计机器学习的方法和基于深度学习的方法, 并详细介绍了每类方法的特点和典型模型; 接下来, 简要概括了金融领域中文NER的公开数据集和工具、评估方法及其应用; 最后, 向读者阐述了目前面临的挑战和未来的发展趋势.

关键词: 自然语言处理, 中文命名实体识别, 金融领域

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

中图分类号:

TP399

徐秋荣, 朱鹏, 罗轶凤, 董启文. 金融领域中文命名实体识别研究进展[J]. 华东师范大学学报（自然科学版）, 2021, 2021(5): 1-13.

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.

图/表 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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