## 2 Related Work
### Data Augmentation  
Numerous studies have explored data augmentation methods such as easy data augmentation [5], where four straightforward processes are implemented to generate diverse data sets that improve classification performance. Adversarial attacks constitute another method for creating data. For example, the TextFooler method [13] generates text by identifying key words and replacing them with semantically and syntactically similar words to create new samples. The BAE method [14] uses a BERT model that incorporates pretraining masked language modeling to predict masked words and integrate with self-designed replacement, and insertion methods. The easy plug-in data augmentation (EPiDA) method [15] employs relative entropy maximization and conditional entropy maximization to evaluate the diversity and quality of generated samples. This method can also be combined with various data augmentation methods and classification models. The easier data augmentation (AEDA) method [16] uses punctuations as augmented data to solve the data scarcity problem. The text autoaugment (TAA) method [17] applies a self-designed algorithm to improve editing-based data augmentation methods. Specifically, TAA leverages compositional augmentation policies, each of which comprises three components. The first component is type, which includes random swapping, random deletion, term frequency-inverse document frequency (TF-IDF) insertion, TF-IDF replacement, and wordnet replace. The second component is probability, which is used to control the probability of usage. The third component is magnitude, which controls the portion of words that are changed. In summary, TAA enhances the generalization ability of a model and reduces the need for human annotation. [PERSON] [18] proposed the use of bidirectional long short-term memory (LSTM) to generate augmented data through input sentences and their labels. [PERSON] et al. [19] performed BERT-masked language model pretraining tasks to randomly mask the words in sentences and output augmented data. [PERSON] et al. [20]proposed the generative pretrained transformer two (GPT-2) model, which can also be used to address problems with scarce data. For example, [PERSON] et al. [21] proposed the language model based data augmentation (i.e., LAMBADA) method, which uses GPT-2, finetunes small sample data sets and generated samples, and selects generated samples through a classifier trained by trained data. [PERSON] et al. [22] proposed a text smoothing method that uses BERT to encode one-hot representations into smoothed and interpolated representations for data augmentation. [PERSON] et al. [23] proposed the DAGAM method, which uses three sentences with the same label as input and employs the T5 generation model [24] to generate augmented sentences. [PERSON] et al. [25] proposed the SRAFBN model, which uses a self-attention mechanism to extract the key information in images so that the generated images can be of a higher quality. [PERSON] et al. [26] used perception technology including knowledge graphs, association rules, and SNA (social network analysis), to extract features from text data, in order to solve the problem that time and operation features cannot be focused.