文本特征和图结点混合增强的图卷积网络文本分类

杨晓奇; 刘伍颖

doi:10.13568/j.cnki.651094.651316.2023.07.05.0004

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文本特征和图结点混合增强的图卷积网络文本分类

信息科学与技术·人工智能专题 | 更新时间：2026-01-21

- 文本特征和图结点混合增强的图卷积网络文本分类
- 文本特征和图结点混合增强的图卷积网络文本分类
- 新疆大学学报（自然科学版中英文） 2024年41卷第1期页码：69-77
- 作者机构：
  
  1. 广东外语外贸大学信息科学与技术学院
  2. 鲁东大学山东省语言资源开发与应用重点实验室
  3. 广东外语外贸大学外国语言学及应用语言学研究中心
- 作者简介：
- 基金信息：
  
  教育部人文社会科学研究规划基金“后深度学习时代低资源语言机器翻译理论与实践研究”（20YJAZH069）,“自由贸易关键小语种语言资源建设理论与实践研究”（20YJC740062）;上海市哲学社会科学规划基金“‘一带一路’关键小语种千万级语言资源建设及精准机器翻译研究”（2019BYY028）
- DOI：10.13568/j.cnki.651094.651316.2023.07.05.0004
  中图分类号： TP391.1
- 纸质出版：2024
- 稿件说明：
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[1]杨晓奇,刘伍颖.文本特征和图结点混合增强的图卷积网络文本分类[J].新疆大学学报(自然科学版)(中英文),2024,41(01):69-77+109.
[1]杨晓奇,刘伍颖.文本特征和图结点混合增强的图卷积网络文本分类[J].新疆大学学报(自然科学版)(中英文),2024,41(01):69-77+109. DOI： 10.13568/j.cnki.651094.651316.2023.07.05.0004.

DOI：10.13568/j.cnki.651094.651316.2023.07.05.0004.

摘要

在BertGCN模型的基础上改进其结构，同时结合文本特征和图结点混合增强的方法，使用新的边权重计算算法BM25+构造图的边．使用R8、R52、Ohsumed和MR这4个常用的公开数据集来验证所提方法的有效性．结果表明：与BertGCN模型及其它基线模型相比，该方法在4个文本分类数据集上的准确率评价指标均有不同程度的提升．

Abstract

The work will improve the structure on the basis of the BertGCN model

not only using a new algorithm to construct the edges of the graph

but also combining a hybrid enhancement of text features and graph nodes. The method not only has some optimization in the edge structure

but also makes fuller use of the extended semantic information of the text in the form of text feature enhancement and graph-enhanced nodes

while retaining the original text features. Four public datasets

R52

Ohsumed and MR which are commonly used

are used to verify the effectiveness of this method. The experimental results show that compared with the BertGCN model and other baselines

the accuracy evaluation metric of the method on the four text classification data sets has been improved to varying degrees.

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Keywords

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