To compare large corpora of legal documents using vector clustering, you first convert the text into numerical representations, apply clustering algorithms to group similar documents, and then analyze the clusters to identify patterns or differences. This approach relies on transforming text into vectors (numerical arrays) that capture semantic or syntactic features, grouping them based on similarity, and comparing the resulting clusters across corpora. For example, you might compare court rulings from two jurisdictions to find thematic overlaps or divergences in legal reasoning.
Start by preprocessing the documents (removing stopwords, normalizing text) and generating vectors. Tools like TF-IDF or word embeddings (Word2Vec, GloVe) convert text into fixed-length vectors, while transformer models like BERT provide context-aware embeddings. For large datasets, dimensionality reduction techniques like PCA or UMAP help manage computational complexity. Once vectors are ready, algorithms like K-means, DBSCAN, or HDBSCAN group documents into clusters. For instance, K-means could cluster patent filings by technical domain (e.g., “biotech” vs. “software”) based on term frequency. The choice of algorithm depends on data size and desired cluster granularity—DBSCAN handles uneven cluster densities better than K-means.
After clustering, compare corpora by analyzing cluster distributions, centroids, or overlap metrics. For example, calculate the cosine similarity between cluster centroids from two sets of contracts to measure how closely their terms align. Alternatively, use visualization tools like t-SNE to plot clusters and observe spatial relationships. If one corpus has a cluster dominated by “privacy regulations” and another lacks it, this highlights a thematic gap. Libraries like scikit-learn (for clustering) and spaCy (for NLP) streamline implementation. Testing different vectorization methods (e.g., comparing TF-IDF vs. BERT embeddings) ensures robustness, as legal language often requires context-aware models to capture nuances like precedent citations or statutory interpretations.