四川大学学报（自然科学版）2024，Vol.61Issue(4)：121-126,6.DOI:10.19907/j.0490-6756.2024.042003
基于分数阶高斯噪声的BERT情感文本分类研究
BERT sentiment text classification research based on fractional gaussian noise
摘要
Abstract
Due to the large number of parameters in the BERT model and the potential for overfitting during its pre-training phase,this paper proposes a method involving the integration of a plug-and-play module based on Fractional Gaussian Noise(fGn)termed FGnTune.This module utilizes fGn to introduce randomness to improve the effectiveness of the BERT pre-trained model in sentiment text classification tasks.fGn is a form of stochastic signal characterized by long-range dependencies and non-stationarity.The integration of fGn noise into the parameters during the fine-tuning phase of BERT enhances the robustness of the model,thereby mitigating the risk of overfitting.Experimental analyses conducted on various network models and datasets demonstrate that the integration of the FGnTune module leads to a modest improvement in accuracy ranging from 0.3%to 0.9%,without the need for additional model parameters or increasing structural complexity.关键词
文本分类/BERT/情感文本/深度学习Key words
Text classification/BERT/Sentiment text/Deep learning分类
计算机与自动化引用本文复制引用
龙雨欣,蒲亦非,张卫华..基于分数阶高斯噪声的BERT情感文本分类研究[J].四川大学学报（自然科学版）,2024,61(4):121-126,6.基金项目
国家自然科学基金面上项目(62171303) （62171303）
分数阶忆阻模拟实现的新标度电路结构及其电气特性变化规律研究(62171303,2022―2025年) （62171303,2022―2025年）
