Abstract: Traditional shallow text clustering methods face challenges such as limited context information,irregular use of words,and few words with actual meaning when clustering short texts,resulting in sparse embedding representations of the text and difficulty in extracting key features.To address these issues,a deep clustering model SSKU(SBERT SimCSE Kmeans Umap) incorporating simple data augmentation methods is proposed in the paper.The model uses SBERT to embed short texts and fine-tunes the text embedding model using the unsupervised SimCSE method in conjunction with the deep clustering KMeans algorithm to improve the embedding representation of short texts to make them suitable for clustering.To improve the sparse features of short text and optimize the embedding results,Umap manifold dimension reduction method is used to learn the local manifold structure.Using K-Means algorithm to cluster the dimensionality-reduced embeddings,and the clustering results are obtained.Extensive experiments are carried out on four publicly available short text datasets,such as StackOverFlow and Biomedical, and compared with the latest deep clustering algorithms.The results show that the proposed model exhibits good clustering performance in terms of both accuracy and standard mutual information evaluation metrics.

Key words: Short text, Deep clustering, Pre-training model, Dimension reduction, Natural language processing