Research on Short Text Classification Based on TextCNN
Tianyu Zhang, Fucheng You
Abstract
Abstract 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.
Topics & Concepts
Computer scienceArtificial intelligenceSample (material)Convolutional neural networkTraining setTraining (meteorology)Translation (biology)Machine learningArtificial neural networkData miningPattern recognition (psychology)ChromatographyMessenger RNAChemistryMeteorologyBiochemistryPhysicsGeneAdvanced Text Analysis TechniquesTopic ModelingText and Document Classification Technologies