短文本分类模型的优化及应用  

Optimization and application of short text classification model

作　　者：雷明珠 邵新慧[1] Lei Mingzhu;Shao Xinhui(College of Science,Northeastern University,Shenyang 110819,China)

机构地区：[1]东北大学理学院,沈阳110819

出　　处：《计算机应用研究》2021年第6期1775-1779,共5页Application Research of Computers

摘　　要：不同于长文本,短文本信息量缺乏,在研究中通常难以获得丰富的语义特征并且难以提取完整的句法特征,因此短文本分类模型的分类效果有待提升。针对这个问题进行了研究,基于ResLCNN模型进行改进,引入神经主题模型,并融合多个神经网络输出特征进行分类。首先,通过神经主题模型提取主题来丰富短文本的信息;其次,将主题信息储存在记忆网络中,并与序列信息进行融合,丰富文本的表示;最后,将其输入具有残差结构的卷积神经网络以及双向GRU中,提取局部以及全局的语义特征,在特征融合之后进行分类。该模型在Google网页搜索公开数据集中取得了较高的准确率和F 1值,表明了改进模型在短文本分类任务中的有效性。Unlike long text classification,short text lacks information,and it is often difficult to obtain rich semantic features and extract complete syntactic features.Therefore,the effect of short text classification model needs to be improved.To solve this problem,this paper proposed a new model based on ResLCNN,the new model which was based on neural topic model and concatenated the features of multiple neural networks.Firstly,it extracted the topics to enrich the information of the short text.Secondly,it stored the topic information in the memory network and fused them with the sequence information as the final expression.Finally,it input the final expression into neural network with residual structure to extract local and global semantic features,and then combined the features together.The model achieves high accuracy in the Google Web search public dataset,which proves the effectiveness of the improved model in short text classification tasks.

关 键 词：短文本 神经主题模型 特征融合 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]