Research on Short Text Classification Based on TextCNN

The TextCNN model is widely used in text classification tasks. It has become a comparative advantage model due to its small number of parameters, low calculation, and fast training speed. However, training a convolutional neural network requires a large amount of sample data. In many cases, there ar...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-01, Vol.1757 (1), p.12092
Main Authors: Zhang, Tianyu, You, Fucheng
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Data augment
Natural language processing
Physics
Text Categorization
TextCNN
Training
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Summary:The TextCNN model is widely used in text classification tasks. It has become a comparative advantage model due to its small number of parameters, low calculation, and fast training speed. However, training a convolutional neural network requires a large amount of sample data. In many cases, there are not enough data sets as training samples. Therefore, this paper proposes a Chinese short text classification model based on TextCNN, which uses back translation to achieve data augment and compensates for the lack of training data. The experimental data shows that our proposed model has achieved good results.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1757/1/012092