A semi-supervised learning financial news classification algorithm

Xiaolong ZHANG; Long ZHI; Jian GAO; Zhongchen MIAO; Yuefeng LIN; Yali XIANG; Yun XIONG

doi:10.11959/j.issn.2096-0271.2022019

您当前的位置：

首页 >

文章列表页 >

A semi-supervised learning financial news classification algorithm

STUDA | 更新时间：2024-06-03

- A semi-supervised learning financial news classification algorithm
- Big data research Vol. 8, Issue 2, Pages: 134-144(2022)
- 作者机构：
  
  1. 复旦大学计算机科学技术学院，上海 210438
  2. 上海市数据科学重点实验室，上海 200438
  3. 上海金融期货信息技术有限公司，上海 200120
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2022019
  CLC： TP312
- Online First：2022-03，
  
  Published：15 March 2022
- 稿件说明：
移动端阅览
Xiaolong ZHANG, Long ZHI, Jian GAO, et al. A semi-supervised learning financial news classification algorithm[J]. Big data research, 2022, 8(2): 134-144.
DOI：

Xiaolong ZHANG, Long ZHI, Jian GAO, et al. A semi-supervised learning financial news classification algorithm[J]. Big data research, 2022, 8(2): 134-144. DOI： 10.11959/j.issn.2096-0271.2022019.

摘要

对金融文本进行分类是一项常见的用于识别金融风险的任务。传统的金融新闻文本分类方法需要大量的已知类别文本来训练分类器，然而标注金融新闻文本标签不仅需要专业的金融背景知识，而且耗时耗力。为了减少对已知类别文本的依赖，提出了一个基于半监督学习的金融文本分类算法，该算法采用有监督学习和无监督学习的一致性训练方式，以更好地利用未知类别的文本数据；针对金融领域文本引入无监督数据增强方法，即对特定任务使用特定目标的数据增强方法，以产生更有效的数据。在多个金融文本数据集上开展的实验证明，相比其他文本分类算法，提出的算法在有效性上有明显提升。

Abstract

Classifying financial texts is a common task for identifying financial risks.Traditional financial news classification requires a large number of labeled texts to train the classifier.However

labeling financial news requires not only professional financial background knowledge

but also time-consuming and labor-intensive.In order to reduce the dependence on labeled text

a semi-supervised learning financial text classification algorithm- SSF (semi-supervised learning financial news classification algorithm) was proposed

which uses a consistent training method of supervised learning and unsupervised learning to improve the use of unlabeled data.And unsupervised data augmentation for financial texts was introduced

that is

use specific target data augmentation methods for specific tasks to generate more effective data.Experiments on multiple financial news data sets were conducted to verify that the proposed SSF algorithm has a significant improvement in effectiveness compared with other text classification algorithms.

关键词

Keywords

references

CHAPELLE O , SCHOLKOPF B , ZIEN E . Semi-supervised learning (Chapelle,O.et al.Eds.; 2006)[book reviews] [J ] . IEEE Transactions on Neural Networks , 2009 , 20 ( 3 ): 542 .

RASMUS A , VALPOLA H , HONKALA M , et al . Semi-supervised learning with ladder network [J ] . arXiv preprint,2015,arXiv:1507.02672 .

LAINE S , AILA T M . Temporal ensembling for semi-supervised learning [J ] . arXiv preprint,2016,arXiv:1610.02242 .

TARVAINEN A , VALPOLA H . Weightaveraged,consistency targets improve semisupervised deep learning results [Z ] . 2017 .

BACHMAN P , ALSHARIF O , PRECUP D . Learning with pseudo-ensembles [J ] . arXiv preprint,2014,arXiv:1412 . 4864 .

MIYATO T , MAEDA S I , KOYAMA M , et al . Virtual adversarial training:a regularization method for supervised and semi-supervised learning [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2019 , 41 ( 8 ): 1979 - 1993 .

SAJJADI M , JAVANMARDI M , TASDIZEN T . Regularization with stochastic transformations and perturbations for deep semi-supervised learning [J ] . arXiv preprint,2016,arXiv:1606.04586 .

CLARK K , LUONG M T , MANNING C D , et al . Semi-supervised sequence modeling with cross-view training [C ] // Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2018 .

VERMA V , KAWAGUCHI K , LAMB A , et al . Interpolation consistency training for semi-supervised learning [J ] . arXiv preprint,2019,arXiv:1903.03825 .

BERTHELOT D , CARLINI N , GOODFELLOW I J , et al . MixMatch:a holistic approach to semi-supervised learning [J ] . arXiv preprint,2019,arXiv:1905.02249 .

XIE Q Z , DAI Z H , HOVY E , et al . Unsupervised data augmentation for consistency training [J ] . arXiv preprint,2019,arXiv:1904.12848 .

RADFORD A , WU J , CHILD R , et al . Language models are unsupervised multitask learners [Z ] . 2019 .

CHEN J A , CHEN J S , YU Z . Incorporating structured commonsense knowledge in story completion [J ] . Proceedings of the AAAI Conference on Artificial Intelligence , 2019 , 33 ( 1 ): 6244 - 6251 .

AKBIK A , BERGMANN T , VOLLGRAF R . Pooled contextualized embeddings for named entity recognition [C ] // Proceedings of the 2019 Conference of the North . Stroudsburg:Association for Computational Linguistics , 2019 .

HOWARD J , RUDER S . Universal language model fine-tuning for text classification [C ] // Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:Association for Computational Linguistics , 2018 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv preprint,2013,arXiv:1301 . 3781 .

LAN Z Z , CHEN M D , GOODMAN S , et al . ALBERT:a lite BERT for self-supervised learning of language representations [J ] . arXiv preprint,2019,arXiv:1909.11942 .

RADFORD A , NARASIMHAN K , SALIMANS T , et al . Improving language understanding by generative pretraining [Z ] . 2018 .

DEVLIN J , CHANG M W , LEE K , et al . BERT:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv preprint,2018,arXiv:1810.04805 .

BEYER L , ZHAI X H , OLIVER A , et al . S4L:self-supervised semi-supervised learning [C ] // Proceedings of 2019 IEEE/CVF International Conference on Computer Vision . Piscataway:IEEE Press , 2019 : 1476 - 1485 .

OLIVER A , ODENA A , RAFFEL C , et al . Realistic evaluation of deep semisupervised learning algorithms [J ] . arXiv preprint,2018,arXiv:1804.09170 .

PENNINGTON J , SOCHER R , MANNING C . Glove:global vectors for word representation [C ] // Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing . Stroudsburg:Association for Computational Linguistics , 2014 .

PETERS M E , NEUMANN M , IYYER M , et al . Deep contextualized word representations [J ] . arXiv preprint,2018,arXiv:1802.05365 .

JOULIN A , GRAVE E , BOJANOWSKI P , et al . FastText.zip:compressing text classification models [J ] . arXiv preprint,2016,arXiv:1612.03651 .

GURURANGAN S , DANG T , CARD D , et al . Variational pretraining for semisupervised text classification [J ] . arXiv preprint,2019,arXiv:1906.02242 .

Views

516

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Study on solving math word problem based on contrastive learning

Fine-tuning and application of large language model in law domain

Construction of a paper-assistant reading system based on machine reading comprehension

Event causality identification network based on knowledge and syntactic structure

A text classification method based on multimodal fusion enhancement

Related Author

ZHANG Yijia

YU Ge

LENG Fangling

YU Minghe

WANG Yuyang

ZHANG Tiancheng

XIAO Yao

SHEN Chenchen

Related Institution

School of Computer Science and Engineering, Northeastern University

School of Computer Science, Fudan University

Institute of Digital Twin, Eastern Institute of Technology

Law School, Northwest University of Political and Law

School of Arts and Science, New York University Shanghai

Postal code：100079
Tel：（010）53879208、53859533 Email：bdr@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：273162 Visits Today：68

⁰