南京师范大学学报（工程技术版）2025，Vol.25Issue(2)：54-68,15.DOI:10.3969/j.issn.1672-1292.2025.02.005
基于词向量模型的短文本分类方法研究综述
Research Review on Short Text Classification Method Based on Word Vector Model
摘要
Abstract
Text classification has important research significance in fields such as abstract generation and information extraction.Compared to long text data,how to efficiently classify short texts is the focus of research.The word vector model can avoid training the model from scratch,accelerating the speed of algorithm research and practice,especially in the field of short text classification.This article analyzes the current research status of mainstream word vector models in the field of short text classification,based on the word vector models used in recent years,from traditional word vector models and pre-trained word vector models.Firstly,a brief overview of the development process of word vector models is provided.The specific applications of word vector models in the field of short text classification are introduced,and their advantages and disadvantages are analyzed.The development prospects of short text classification in the post word vector era are given.Finally,the limitations and future development directions of current word vector models in short text classification are discussed.关键词
文本挖掘/词向量/短文本分类/预训练模型Key words
text mining/word vector/short text classification/pre-trained models分类
计算机与自动化引用本文复制引用
李晨,刘纳,郑国风,杨杰,道路..基于词向量模型的短文本分类方法研究综述[J].南京师范大学学报（工程技术版）,2025,25(2):54-68,15.基金项目
宁夏自然科学基金项目(2021AAC03224)、北方民族大学校级科研项目(2024XYZJK01)、北方民族大学研究生创新项目(YCX23167). （2021AAC03224）
