<ul><li>This article introduces text clustering and its application in identifying clusters of related topics without manual reading of thousands of research abstracts.</li><li>The article discusses the process of converting text into numerical representations using embedding models, selecting a suitable clustering model (stella-en-400M-v5), and reducing the dimensionality using UMAP.</li><li>Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to cluster the reduced embeddings, resulting in 159 clusters.</li><li>The clusters are validated through manual inspection and 3D visualization, showcasing the successful organization of 44,949 arXiv NLP papers into semantically coherent groups.</li></ul>

From Text to Insights: Hands-on Text Clustering and Topic Modeling with LLMs — Part 1

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