# A Small-Sample Text Classification Model Based on Pseudo-Label Fusion Clustering Algorithm
## 1 Introduction
Therefore, to solve the training dilemma of little labeled data, a holistic perspective is needed. The introduction of new feature information has become a new research direction for this problem. [PERSON] et al. [11] introduced new features from unlabeled data, such as lexical features, word clustering features of Word2 Vec, and clustering features with constraints. [PERSON] et al. [12] proposed a hybrid approach of the LDA algorithm and Word2 Vec. This method classifies documents in an unsupervised way, obtains the Gibbs sampling results of LDA, and assigns the results to documents as the most representative words in the topic by means of a cosine distance metric, which results in new features. Self-training is likewise a good solution [13]. Self-training uses unlabeled data to generate pseudo-results that serve as new labels for the data and are used for further training. However, traditional self-training does not perform sample selection and does not consider the noise in the pseudo-labels generated during the training process. Noisy data were masking the true relationship between unlabeled data and their categories. This may lead to error accumulation. To address this problem, [PERSON] et al. [14] reduced the sensitivity of the learning algorithm to noise in the labeled data by automatically generating a summary, effectively improving the accuracy of model training. However, noise is difficult to avoid, some processes, such as data augmentation, can generate noise at any time, and noise can diversify the data to improve model learning, and blanket rejection of noise is not always beneficial for model training. Therefore, we propose consistent training for pseudo-labeling, making full use of unlabelled data to introduce new feature information while using noise to improve model robustness and avoid model overfitting. Besides, [PERSON] et al. [15] proposed a short text classification method based on weighted word vector representation. This method uses the expected cross-entropy to extract the strong category feature set from the labeled data and then generates word vectors as feature vectors to increase the semantic information of short texts and reduce their high-dimensional sparsity. Following these ideas, after being inspired by the use of clustering algorithms to obtain labels in image classification [16], we improve the data pre-processing process and design the implementation of a method to obtain pseudo-labels based on text clustering. After combining the advantages and disadvantages of various algorithms in current semi-supervised learning, we propose a small-sample text classification model based on a pseudo-label fusion clustering algorithm (STCPC) by combining the advantages and disadvantages of various algorithms in current semi-supervised learning. The STCPC algorithm combines the advantages of the above methods, fully exploits the effective information of a large amount of unlabeled data, and uses a small amount of labeled data to achieve efficient semi-supervised text classification. The main contributions of our paper are summarized as follows:  
1. [label=(0)]
2. Combining the similarity information between labeled and unlabeled data, we propose a pseudo-labeling algorithm based on text clustering, where the pseudo-labels are formed by mining the latent features of the unlabeled data. It is also proposed to use consistent training for the pseudo-labels to improve the robustness of the model. 3. Combining common data enhancement methods with EPiDA framework and made compatible with our enhanced methodology to filter out tagged enhanced data with high overall diversity and quality scores. 4. Using the BERT model as the basis for implementing our enhanced approach, the improvement effect is demonstrated in the experiments of four datasets, and it is also improved in comparison with other algorithms.