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This research advances unsupervised clustering for legal documents by fusing topic semantics with relational graph embeddings on a unified hypersphere. The proposed framework effectively handles domain-specific terminology and varying document lengths, outperforming standard baselines like LDA and TF-IDF across major legal benchmarks.
Hybrid Topic-Semantic Labeling and Graph Embeddings for Unsupervised Legal Document Clustering
arXiv:2509.00990v2 Announce Type: replace-cross Abstract: Legal documents pose substantial challenges for unsupervised knowledge discovery due to specialized domain terminology, multi-scale document lengths, and scarce expert annotations. This paper introduces Topic-Graph Spherical Fusion (TGSF), a dual-manifold representation learning framework for unsupervised legal document clustering that unifies dense topic-semantic discovery and relational graph embeddings on a joint unit hypersphere. Instantiating dense topic discovery via Top2Vec and biased second-order random walks via Node2Vec, TGSF constructs a connected multi-relational document-topic graph combining multi-topic centroid bridges with temperature-sharpened semantic k-Nearest Neighbor (k-NN) document edges, guaranteeing a single connected component. The resulting L2-normalized semantic and graph representations are fused via modality-weighted concatenation onto a joint unit hypersphere prior to KMeans partitioning, decomposing squared chordal distance into a convex combination of semantic and structural cosine similarities. Evaluations across three legal benchmarks (ACORD, CUAD, and LEDGAR) show that TGSF outperforms semantic-only, structural-only, Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and TF-IDF baselines. Across 216 factorial grid configurations and 240 Bayesian TPE and Multi-Objective NSGA-II Pareto optimization trials per dataset (720 trials total), TGSF improves Normalized Mutual Information (NMI) from 0.606 to 0.653 on LEDGAR (ARI = 0.425), 0.228 to 0.284 on ACORD (ARI = 0.260), and 0.535 to 0.570 on CUAD (ARI = 0.282). Functional ANOVA and Pareto frontier diagnostics further characterize hyperparameter sensitivity for reproducible legal text mining.
Abstract:Legal documents pose substantial challenges for unsupervised knowledge discovery due to specialized domain terminology, multi-scale document lengths, and scarce expert annotations. This paper introduces Topic-Graph Spherical Fusion (TGSF), a dual-manifold representation learning framework for unsupervised legal document clustering that unifies dense topic-semantic discovery and relational graph embeddings on a joint unit hypersphere. Instantiating dense topic discovery via Top2Vec and biased second-order random walks via Node2Vec, TGSF constructs a connected multi-relational document-topic graph combining multi-topic centroid bridges with temperature-sharpened semantic k-Nearest Neighbor (k-NN) document edges, guaranteeing a single connected component. The resulting L2-normalized semantic and graph representations are fused via modality-weighted concatenation onto a joint unit hypersphere prior to KMeans partitioning, decomposing squared chordal distance into a convex combination of semantic and structural cosine similarities. Evaluations across three legal benchmarks (ACORD, CUAD, and LEDGAR) show that TGSF outperforms semantic-only, structural-only, Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and TF-IDF baselines. Across 216 factorial grid configurations and 240 Bayesian TPE and Multi-Objective NSGA-II Pareto optimization trials per dataset (720 trials total), TGSF improves Normalized Mutual Information (NMI) from 0.606 to 0.653 on LEDGAR (ARI = 0.425), 0.228 to 0.284 on ACORD (ARI = 0.260), and 0.535 to 0.570 on CUAD (ARI = 0.282). Functional ANOVA and Pareto frontier diagnostics further characterize hyperparameter sensitivity for reproducible legal text mining.
21 pages, 11 figures, 5 tables. Major revision (v2): formalizes Topic-Graph Spherical Fusion(TGSF) with a connected multi-relational document-topic graph and L2 spherical fusion; expands evaluation across LEDGAR, ACORD, and CUAD with unimodal ablations; adds 648 factorial grid runs and 720 Bayesian TPE and NSGA-II Pareto optimization trials with fANOVA analysis
Machine Learning (stat.ML); Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2509.00990 [stat.ML]
(or arXiv:2509.00990v2 [stat.ML] for this version)
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