1. 基于卷积神经网络和贝叶斯分类器的句子分类模型.
- Author
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李文宽, 刘培玉, 朱振方, and 刘文锋
- Subjects
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ARTIFICIAL neural networks , *PRINCIPAL components analysis , *FEATURE extraction , *DEEP learning , *STATE universities & colleges , *CLASSIFICATION - Abstract
The traditional sentence classification model has many disadvantages such as complex feature extraction process and low classification accuracy. This paper used the advantages of the popular deep learning model based convolutional neural network in feature extraction, combined with the traditional sentence classification method, proposed a sentence classification model based on convolutional neural network and Bayesian classifier. The model first used convolutional neural network to extract text features, and secondly used principal component analysis method to reduce the dimensionality of text features. Finally, Bayesian classifier were used to classify sentences. The experimental results show that on Cornell University's public film review dataset and Stanford Sentiment Treebank dataset, the proposed model is superior to the model using only deep learning or the traditional sentence classification model. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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