摘要
文本分类指的是在制定文本的类别体系下,让计算机学会通过某种分类算法将待分类的内容完成分类的过程.与文本分类有关的算法已经被应用到了网页分类、数字图书馆、新闻推荐等领域.本文针对短文本分类任务的特点,提出了基于多神经网络混合的短文本分类模型(Hybrid Short Text Classical Model Base on Multi-neural Networks).通过对短文本内容的关键词提取进行重构文本特征,并作为多神经网络模型的输入进行类别向量的融合,从而兼顾了FastText模型和TextCNN模型的特点.实验结果表明,相对于目前流行的文本分类算法而言,多神经网络混合的短本文分类模型在精确率、召回率和F1分数等多项指标上展现出了更加优越的算法性能.
Text classification refers to the process of letting a computer learn to complete the classification of content by some classification algorithm under the classification system of text.Algorithms related to text classification have been applied to web classification,digital libraries,news recommendation,and other fields.Based on the characteristics of short text classification tasks,this study proposes a hybrid short text classical model based on multi-neural networks.By reconstructing the text features of the keywords extracted from the short text content,and using the vector fusion as the input of the multi-neural network model,the characteristics of the FastText model and the TextCNN model are taken into account.The experimental results show that compared with the current popular text classification algorithms,the multineural network hybrid short text classification model shows more superior algorithm performance on multiple indicators such as accuracy,recall,and F1 score.
作者
侯雪亮
李新
陈远平
HOU Xue-Liang;LI Xin;CHEN Yuan-Ping(Computer Network Information Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《计算机系统应用》
2020年第10期9-19,共11页
Computer Systems & Applications
基金
中国科学院信息化建设专项(XXH13504-01)
作者简介
通讯作者:李新,E-mail:lixin@cnic.cn
