Research on Policy Tools Classification Based on ChatGPT Augmentation and Supervised Contrastive Learning

被引：0
|
作者：
Hu, Zhiqiang [1 ]
Li, Pengjun [1 ]
Wang, Jinlong [1 ]
Xiong, Xiaoyun [1 ]
机构：
[1] School of Information and Control Engineering, Qingdao University of Technology, Shandong, Qingdao,266525, China
来源：
Computer Engineering and Applications | 2024年 / 60卷 / 07期
关键词：
The classification of policy tools is an important dimension in the quantification and analysis of policy texts. Due to the scarcity of training data; models are prone to overfitting; resulting in reduced prediction confidence and an increased risk of misclassification. Therefore; policy tool classification method based on ChatGPT augmentation and supervised contrastive learning is proposed. The method consists of two stages: pre-training language model fine-tuning and ChatGPT decision augmentation. In the first stage; ChatGPT; a large language model; augments policy texts to increase the training dataset; while supervised contrastive learning fine-tune the RoBERTa model to improve classification performance. In the second stage; ChatGPT assists in the decision-making process for low confidence texts through the pretrained language model and reduces the risk of misclassifying similar texts. Experiments on the digital industry policy tools classification dataset and the Tnews dataset show that the proposed method surpasses mainstream research approaches and can effectively improve the performance of the base model; with a more significant improvement observed when the training samples are limited. © 2024 Journal of Computer Engineering and Applications Beijing Co; Ltd; Science Press. All rights reserved;
D O I：
10.3778/j.issn.1002-8331.2308-0354
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：292 / 305
相关论文
共 50 条
  • [41] Supervised Contrastive Learning for Few-Shot Action Classification
    Han, Hongfeng
    Fei, Nanyi
    Lu, Zhiwu
    Wen, Ji-Rong
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT III, 2023, 13715 : 512 - 528
  • [42] Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification
    Liu, Dongxin
    Wang, Peng
    Wang, Tianshi
    Abdelzaher, Tarek
    2021 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM 2021), 2021,
  • [43] A balanced supervised contrastive learning-based method for encrypted network traffic classification
    Ma, Yuxiang
    Li, Zhaodi
    Xue, Haoming
    Chang, Jike
    COMPUTERS & SECURITY, 2024, 145
  • [44] Contrastive self-supervised learning for neurodegenerative disorder classification
    Gryshchuk, Vadym
    Singh, Devesh
    Teipel, Stefan
    Dyrba, Martin
    ADNI Study Grp
    AIBL Study Grp
    FTLDNI Study Grp
    FRONTIERS IN NEUROINFORMATICS, 2025, 19
  • [45] Supervised Contrastive Learning-Based Unsupervised Domain Adaptation for Hyperspectral Image Classification
    Li, Zhaokui
    Xu, Qiang
    Ma, Li
    Fang, Zhuoqun
    Wang, Yan
    He, Wenqiang
    Du, Qian
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [46] SWIN transformer based contrastive self-supervised learning for animal detection and classification
    L. Agilandeeswari
    S. Divya Meena
    Multimedia Tools and Applications, 2023, 82 : 10445 - 10470
  • [47] Multi-Scale Contrastive Learning based Weakly Supervised Learning for Remote Sensing Scene Classification
    Peng, Rui
    Zhao, Wenzhi
    Zhang, Liqiang
    Chen, Xuehong
    Journal of Geo-Information Science, 2022, 24 (07) : 1375 - 1390
  • [48] Contrastive Continuity on Augmentation Stability Rehearsal for Continual Self-Supervised Learning
    Cheng, Haoyang
    Wen, Haitao
    Zhang, Xiaoliang
    Qiu, Heqian
    Wang, Lanxiao
    Li, Hongliang
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 5684 - 5694
  • [49] Research on Abnormal Traffic Classification of Web Camera Based on Supervised Learning and Semi - supervised Learning
    Yi, Long
    Shi, Yijie
    PROCEEDINGS OF 2017 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC), 2017, : 547 - 551
  • [50] Graph-based Text Classification by Contrastive Learning with Text-level Graph Augmentation
    Li, Ximing
    Wang, Bing
    Wang, Yang
    Wang, Meng
    ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2024, 18 (04)
12345