Open AccessResearch on Short Text Classification Based on TextCNN
Author(s) -
Tianyu Zhang,
Fei You
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012092
Subject(s) - computer science , artificial intelligence , sample (material) , convolutional neural network , training set , training (meteorology) , translation (biology) , machine learning , artificial neural network , data mining , pattern recognition (psychology) , biochemistry , chemistry , physics , chromatography , meteorology , messenger rna , gene
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.