Sentence Transformers can effectively cluster documents or perform topic modeling by converting text into dense vector representations that capture semantic meaning. These models, trained to generate embeddings where similar sentences are close in vector space, allow you to map entire documents or paragraphs into fixed-length vectors. Once embeddings are generated, standard clustering algorithms like K-means, DBSCAN, or hierarchical clustering can group documents based on their vector similarity. For example, using a pre-trained model like all-MiniLM-L6-v2, you could convert 10,000 news articles into 384-dimensional vectors, then apply K-means to identify clusters like “sports,” “politics,” or “technology” based on proximity in the embedding space. This approach often outperforms traditional methods like TF-IDF because embeddings capture context and synonyms better.
For topic modeling, Sentence Transformers can complement techniques like Latent Dirichlet Allocation (LDA) by providing a semantic layer. Instead of relying solely on word co-occurrence statistics (as LDA does), you could first cluster documents using their embeddings and then extract keywords from each cluster. For instance, after clustering tech articles, you might find subtopics like “AI ethics” or “cloud computing” by analyzing frequent terms or phrases in the most representative documents of each cluster. Tools like Top2Vec automate this by combining embeddings with term frequency analysis to assign human-interpretable labels to clusters. Additionally, reducing embedding dimensions with UMAP or t-SNE can help visualize clusters and validate their coherence before finalizing topics.
Practical implementation involves preprocessing (tokenization, removing stopwords), choosing an appropriate model, and tuning clustering parameters. Libraries like sentence-transformers and scikit-learn simplify embedding generation and clustering. For large datasets, consider batch processing and approximate nearest neighbor libraries like FAISS or Annoy to speed up similarity searches. A common pitfall is assuming clusters directly map to topics—manual validation is often needed to refine results. For example, a cluster of embedded medical documents might initially mix “pediatrics” and “geriatrics,” requiring finer-grained analysis. By iterating on parameters like cluster count or distance thresholds, you can achieve meaningful groupings that align with domain-specific topics.