融合标签语义嵌入和图卷积的短文本特征扩展及分类方法

张灵; 李荣臻; 郑苏

doi:10.12052/gdutxb.220132

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融合标签语义嵌入和图卷积的短文本特征扩展及分类方法

计算机科学与技术 | 更新时间：2026-05-27

- 融合标签语义嵌入和图卷积的短文本特征扩展及分类方法
- Short Text Feature Extension and Classification Method Based on Semantic Embedding of Tags and Graph Convolution Network
- 广东工业大学学报 2024年41卷第1期页码：69-78
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 宁夏大学教育学院,宁夏,银川,750001
- 作者简介：
  
  张灵(1968−)，女，教授，博士，主要研究方向为数据挖掘、计算机视觉、无线传感，E-mail：june4567@21cn.com
- 基金信息：
  
  广东省交通运输厅科技项目(科技-2016-02-030)
- DOI：10.12052/gdutxb.220132
  中图分类号： TP391
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
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张灵, 李荣臻, 郑苏. 融合标签语义嵌入和图卷积的短文本特征扩展及分类方法[J]. 广东工业大学学报, 2024, 41(1): 69-78.

Zhang Ling, Li Rong-zhen, Zheng Su. Short Text Feature Extension and Classification Method Based on Semantic Embedding of Tags and Graph Convolution Network[J]. 2024, 41(1): 69-78.
张灵, 李荣臻, 郑苏. 融合标签语义嵌入和图卷积的短文本特征扩展及分类方法[J]. 广东工业大学学报, 2024, 41(1): 69-78. DOI： 10.12052/gdutxb.220132.

Zhang Ling, Li Rong-zhen, Zheng Su. Short Text Feature Extension and Classification Method Based on Semantic Embedding of Tags and Graph Convolution Network[J]. 2024, 41(1): 69-78. DOI： 10.12052/gdutxb.220132.

摘要

针对短文本长度过短、关键词偏少和标签信息利用不足造成的分类过程中面临特征稀疏和语义不明确的问题，提出了融合标签语义嵌入的图卷积网络模型。首先，在传统的术语频率和逆文档频率算法基础上，提出了融合单词所属文本的类间、类内分布关系的全局词频提取算法。其次，利用融合标签嵌入的方法，将每条训练文本与相对应的标签引入到同一个特征空间内，通过筛选聚合提取更能突显文本类别的近义词嵌入，作为文本图的文档节点的嵌入表示。最后，将文本图输入到图卷积神经网络学习后，获得的特征与预训练模型提取文本上下文的特征相融合，提升短文本的分类质量以及整个模型的泛化能力，在4个短文本数据集MR、web_snippets、R8和R52上对本文模型和14个基线算法进行了对比实验，结果表明本文提出的模型相比于对比模型具有更优的结果，在识别精度、召回率以及

值上有着更好的表现。

Abstract

In short text classification

too short text length

fewer keywords and underutilization of the label information leads to the severe problems of sparse features and ambiguous semantics

which can affect the performance of short text classification. Agraph convolution network model based on tag semantic embedding is proposed for the problem. Firstly

according to TF/IDF

a new word frequency method is proposed

which comprehensively considers the inter-class and intra-class distribution of words in the global corpus. Then

through By label embedding method

each training

text with the corresponding label is mapped into one feature space in the text graph. After filtering and aggregation in one feature space

synonyms embedded of label information can highlight the category representation. Finally

the text graph is input into the graph convolution neural network to learn new feature. Both the learned new feature Both the learned new feature and the features from the pre-training model can improve the classification accuracy of short texts and the generalization ability of the whole model. We choose four short text datasets such as MR

web_snippets

R8 and R52

to evaluate the performance of our proposed algorithm and fourteen benchmark models. The experimental results show that the proposed model in this paper is superior to others in classification accuracy

recall ratio and

-score.

关键词

Keywords

references

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