Chinese News Text Classification Method Based On Attention Mechanism

Jinjun Ruan, Jonathan M. Caballero, Ronaldo Juanatas
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引用次数: 1

Abstract

Combining the convolution neural network (CNN) model and bidirectional long short-term memory (BiLSTM) model, an ATT-CN-BILSTM Chinese news classification model is proposed based on the attention mechanism. The model uses the attention mechanism to improve the feature extraction process of CNN and BiLSTM. After cancelling the CNN pooling layer, it pays attention to the critical local features obtained by CNN convolution according to the timing features output by BiLSTM, giving full play to the respective advantages of CNN and BiLSTM models. The experimental results on Thucnews dataset show that the accuracy of the model for Chinese news text classification is 97.87%, and the recall rate and F1 score are better than the comparison model.
基于注意机制的中文新闻文本分类方法
将卷积神经网络(CNN)模型与双向长短期记忆(BiLSTM)模型相结合,提出了一种基于注意机制的ATT-CN-BILSTM中文新闻分类模型。该模型利用注意机制改进了CNN和BiLSTM的特征提取过程。在取消CNN池化层后,根据BiLSTM输出的定时特征,关注CNN卷积得到的关键局部特征,充分发挥CNN和BiLSTM模型各自的优势。在Thucnews数据集上的实验结果表明,该模型对中文新闻文本分类的准确率为97.87%,召回率和F1分数均优于比较模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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