A Chinese Text Classification Method With Low Hardware Requirement Based on Improved Model Concatenation
This work addresses the need for efficient text classification in resource-limited settings, though it is incremental as it builds on established models.
The paper tackled the problem of improving Chinese text classification accuracy under low hardware constraints by designing an improved concatenation model combining TextCNN, LSTM, and Bi-LSTM, achieving a 2% higher accuracy compared to existing ensemble methods.
In order to improve the accuracy performance of Chinese text classification models with low hardware requirements, an improved concatenation-based model is designed in this paper, which is a concatenation of 5 different sub-models, including TextCNN, LSTM, and Bi-LSTM. Compared with the existing ensemble learning method, for a text classification mission, this model's accuracy is 2% higher. Meanwhile, the hardware requirements of this model are much lower than the BERT-based model.