| 注册
首页|期刊导航|计算机工程|基于对比学习和注意力机制的文本分类方法

基于对比学习和注意力机制的文本分类方法

钱来 赵卫伟

计算机工程2024,Vol.50Issue(7):104-111,8.
计算机工程2024,Vol.50Issue(7):104-111,8.DOI:10.19678/j.issn.1000-3428.0068132

基于对比学习和注意力机制的文本分类方法

Text Classification Method Based on Contrastive Learning and Attention Mechanism

钱来 1赵卫伟1

作者信息

  • 1. 国防科技大学信息通信学院,湖北 武汉 430010
  • 折叠

摘要

Abstract

Text classification is a basic task in the field of natural language processing and plays an important role in information retrieval,machine translation,sentiment analysis,and other applications.However,most deep learning models do not fully consider the rich information in training instances during inference,resulting in inadequate text feature learning.To leverage training instance information fully,this paper proposes a text classification method based on contrastive learning and attention mechanism.First,a supervised contrastive learning training strategy is designed to optimize the retrieval of text vector representations,thereby improving the quality of the retrieved training instances during the inference process.Second,an attention mechanism is constructed to learn the attention distribution of the obtained training text features,focusing on adjacent instance information with stronger relevance and capturing more implicit similarity features.Finally,the attention mechanism is combined with the model network,fusing information from adjacent training instances to enhance the ability of the model to extract diverse features and achieve global and local feature extraction.The experimental results demonstrate that this method achieves significant improvements on various models,including Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(BiLSTM),Graph Convolutional Network(GCN),Bidirectional Encoder Representations from Transformers(BERT),and RoBERTa.For the CNN model,the macro F1 value is increased by 4.15,6.2,and 1.92 percentage points for the THUCNews,Toutiao,and Sogou datasets,respectively.Therefore,this method provides an effective solution for text classification tasks.

关键词

文本分类/深度模型/对比学习/近似最近邻算法/注意力机制

Key words

text classification/deep model/contrastive learning/approximate nearest neighbor algorithm/attention mechanism

分类

信息技术与安全科学

引用本文复制引用

钱来,赵卫伟..基于对比学习和注意力机制的文本分类方法[J].计算机工程,2024,50(7):104-111,8.

基金项目

国家部委基金. ()

计算机工程

OA北大核心CSTPCD

1000-3428

访问量0
|
下载量0
段落导航相关论文