PRISM: LLM-Guided Semantic Clustering for High-Precision Topics

5 Steps

1. 1

   Prepare Text Corpus: Collect and preprocess your unstructured text data. Clean, normalize, and segment the text into meaningful units (e.g., sentences, paragraphs) suitable for encoding.

2. 2

   Select/Fine-Tune Sentence Encoder: Choose a pre-trained sentence encoding model (e.g., Sentence-BERT, a transformer-based model). For domain-specific precision, fine-tune this model on a relevant dataset to enhance its contextual understanding, leveraging principles of LLM guidance.

3. 3

   Generate Semantic Embeddings: Use the selected or fine-tuned sentence encoder to transform your preprocessed text units into high-dimensional semantic embeddings. These vectors represent the contextual meaning of each text unit.

4. 4

   Perform Latent Semantic Clustering: Apply a clustering algorithm to the generated embeddings. Techniques like UMAP for dimensionality reduction followed by HDBSCAN or K-Means can effectively group semantically similar embeddings into latent topics.

5. 5

   Interpret and Refine Topics: Analyze the clusters to derive meaningful topic labels. Evaluate the precision and coherence of the identified topics, refining parameters or re-evaluating the encoding/clustering steps as needed to achieve high-precision topic identification.