# Chinese News Text Classification Method via Key Feature Enhancement
## 4 Experiments
### Discussion  
In order to illustrate the advantages of this method from different aspects, an overview is mainly developed from the following four questions. Question 1: In the news text classification task, is it necessary to carry out data enhancement on the samples? According to the experimental results in Section 4.4, it can be found that data enhancement is very necessary, although data enhancement processing will introduce additional noise to a small number of samples and reduce the classification performance. However, in general, as long as the data enhancement strategy is good enough, the overall performance of the model can be significantly improved through data enhancement technology. Question 2: Is it better for the data augmentation strategy to augment only some samples or to augment all samples? In our experiments, it can be seen that for long text sample data, it is better to use data for enhancement only for samples whose original content length is less than 500. There are two main reasons: the first is that the time cost of partial sample data enhancement is lower than that of all sample data enhancement; the second is that we found, through comparative experiments, that after all sample data is enhanced, the overall classification performance of the model is lower than that of some samples after enhancement. The average F1-score is 97%, but it is still slightly higher than the original CNN model without data enhancement. Question 3: The new sample dataset obtained by enhancing part of the sample data sees a significant improvement for the KFE-CNN model, but is it equally effective for other models? In order to answer this question, we select the RNN skeleton model and carry out comparative experiments on #DataSetB and find that the comprehensive performance of the KFE-RNN model on #DataSetB has also been significantly improved. Compared with the original RNN model, the average accuracy of KFE-RNN has increased by 1.7% to 96.19%, the min-loss value has dropped to 0.14 and the average F1-score has increased by 2% to reach 96%, which fully demonstrates that some sample data enhancement can effectively improve the overall performance of related models. The relevant comparative experimental results for Question 2 and Question 3 are shown in Figure 5a,b.  
Question 4: When using a deep learning model to solve the text classification task, will quantized features have a great impact on the overall performance and model size? Generally speaking, after the vectors are quantized, the accuracy of the model decreases due to the loss of precision. However, because the KFE-CNN method uses high-quality key feature semantic extension technology to expand many additional features, even if the quantization operation reduces the accuracy, the experimental results show that high-quality feature engineering can bring about a greater performance improvement. The overall performance of the method is not greatly reduced but has been improved. In addition, compared to the original CNN text classification method combined with the Bert pre-training model to complete the feature representation, the overall model size is more  
Figure 5: The experimental results of (**a**) KFE-CNN and (**b**) KFE-RNN models on different datasets.  
than 100 MB. Because the KFE-CNN model uses zero-one binary integer quantization feature representation technology, there is no need to introduce an additional pre-trained language model to compress the overall model size to less than 10 MB.  
**Shortcomings summary.** First of all, the KFE-CNN model has only been validated for Chinese text and has not been analyzed for feasibility in other languages. Secondly, our model currently performs well in the classification of news texts, but it has not been verified in other types of text. Thirdly, the current key feature enhancement module relies on LAC tools, but we could consider replacing them with analysis tools supporting other languages.