Semi-supervised Multi-label Classification Method for Financial Events

• Published in: Shuju Caiji yu Chuli = Journal of Data Acquisition and Processing Vol. 39; no. 2; p. 385
• Main Authors: Yang, Zhuofeng, Li, Yang, Li, Deyu
• Format: Journal Article
• Language: Chinese
• Published: Nanjing Nanjing University of Aeronautics and Astronautics 01.01.2024
• Subjects: Classification; Data augmentation; Deep learning; Divergence; Financial services; Labels; Machine learning; Natural language processing; Words (language)
• ISSN: 1004-9037
• DOI: 10.16337/j.1004-9037.2024.02.011

With the continuous development of the digital financial service industry, the Internet and financial service systems have accumulated a large amount of text data. The automatic classification of financial events described in the financial text is a realistic demand of financial technology, and also a widespread concern in the field of natural language processing and machine learning. At present, the deep learning method has been widely used in text classification. Addressing the issues of lack of labeled data in multi label classification of financial events in text data, frequent resource consumption of existing deep learning methods, and failure to explore the specific characteristics of financial event texts, a semi-supervised multi-label classification method of financial events is proposed by using ALBERT, TextCNN and other presentation tools, introducing the subject word attention mechanism. Firstly, the problem of insufficient labeled data is alleviated through unsupervised data augmentation(UDA) meth