# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 2 Related Work
### Few-Shot Learning  
Our research focuses on scenarios with limited annotated data, also known as small-sample learning. Researchers have turned the method of solving small sample problems into few-shot learning, which aims to learn from a small number of samples to solve problems [17]. In earlier research results, ew-shot learning [18; 19] was mostly applied in computer vision and based on statistical learning methods [20]. For example, [PERSON] et al. [21] proposed a Bayesian network with an approach that captures the relationships between attributes using probability distributions, thus allowing a small number of labeled image samples to yield more information. With the development of deep neural networks, Few-shot learning has also seen some research and development in the field of natural language processing (NLP) [22]. According to the different approaches and goals taken, the most common small sample learning methods can be classified into three types: fine-tuning, data augmentation, and transfer learning.  
Fine-tuning-based approach. The process is to learn from a large general corpus, train a classification model, and then fine-tune the parameters. For example, [PERSON] et al. [23] proposed a fine-tuning approach for the small-sample problem to enhance the model capability by using a lower learning rate during training and an adaptive gradient optimizer during the fine-tuning phase. Such approaches often suffer from overfitting problems because the target data are too different from the pre-trained data, and a small amount of data does not reflect the true distribution of a large amount of data well.  
Data-augmentation-based approach. The process is to enhance the classification features in the target dataset or expand the target dataset annotation information using auxiliary information or aids for the model to extract the dataset features. For example, [PERSON] et al. [24] proposed the easy data augmentation method EDA (easy data augmentation), which uses four methods: synonym replacement, random insertion, random exchange, and random deletion to expand the text. Because of its rapidity and effectiveness, EDA is widely used. However, this type of approach causes a loss of information because it does not utilize the much larger amount of unlabeled data.  
Transfer-learning-based approach. The process is to transfer the information learned by the model in advance to a new knowledge domain. In transfer learning, the existing knowledge is called the source domain, and the new knowledge to be learned is called the target domain, and as long as the source and target domains are related, the knowledge and features learned in the source domain can help train the classification model in the target domain and realize the transfer of knowledge between different domains [25]. [PERSON] et al. [26] proposed a prototypical network that maps images into vectors and uses the average of sample vectors belonging to the same category as the prototype of that category. The correlation between the training data and the test samples is used to continuously train the model to make the samples of the same category closer to each other, thus achieving a better prediction effect. However, in many cases, the importance of the samples used to calculate the prototype is different, especially when the samples are noisy data. For this reason, [PERSON] et al. [27] proposed that the model can be used to integrate the information of all images in the support set so as to find the most discriminative features and enrich the extracted feature information. In addition, transfer learning-based methods such as meta-learning and graphical neural network methods are available, which fully exploit and match the correlation between source and target domains to improve the classification effect to different degrees, but because of this, they are inevitably affected by the differences between source and target domains.