Comprehensive research and implementation of two specialized sentence boundary detection libraries optimized for the unique challenges of legal text processing, supporting large-scale applications in e-discovery, due diligence, and legal research.

Research Overview

This paper addresses a critical challenge in legal NLP: accurately detecting sentence boundaries in complex legal documents. Poor segmentation can fragment related legal concepts, undermining retrieval-augmented generation (RAG) systems and other legal AI applications.

Publication

“Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary”

Authors: Michael J. Bommarito II, Daniel Martin Katz, Jillian Bommarito
Published: 2025
Available on arXiv

Evaluation Dataset

The research evaluated performance on:

25,000+ legal documents
197,000 annotated sentence boundaries
Diverse legal text types: contracts, court opinions, regulations
Real-world complexity: citations, enumerations, quotations

NUPunkt: Rule-Based Precision

Key Features

Legal Knowledge Base: 4,000+ domain-specific abbreviations
Structural Understanding: Hierarchical enumeration handling
Statistical Collocation: Multi-word expression detection
Zero Dependencies: Pure Python implementation

Performance

91.1% precision on legal text
10 million chars/second throughput
29-32% improvement over general tools
Minutes vs hours for large collections

CharBoundary: Machine Learning Approach

Model Variants

Small: 518K chars/second, balanced performance
Medium: 602K chars/second, optimal speed
Large: 748K chars/second, highest F1 score (0.782)

Technical Approach

Character-level feature engineering
Random Forest classification
Legal-specific training data
Optional ONNX acceleration

Research Contributions

Methodological Innovations

Domain-Specific Evaluation: First large-scale benchmark for legal SBD
Comparative Analysis: Systematic comparison of rule-based vs ML approaches
Performance Metrics: Focus on precision for legal applications
Scalability Testing: Real-world throughput measurements

Key Findings

Legal text requires specialized handling
Precision matters more than recall for RAG
Hybrid approaches show promise
CPU-only solutions are viable at scale

Practical Impact

The research demonstrates:

RAG Improvement: Better chunk boundaries for retrieval
Cost Reduction: CPU-based processing feasibility
Time Savings: Orders of magnitude faster processing
Accuracy Gains: Significant precision improvements

Implementation Resources

Code Repository

Contains:

Evaluation scripts
Benchmark datasets
Performance testing tools
Comparison notebooks

Interactive Demo

Available at sentences.aleainstitute.ai:

Compare algorithms side-by-side
Test on custom legal text
Visualize boundary decisions
Download results

Broader Significance

This research establishes:

Importance of domain-specific NLP tools
Viability of lightweight solutions
Standards for legal text processing
Foundation for future legal AI systems

The paper’s contributions extend beyond technical achievements, providing practical tools that legal technologists can immediately deploy in production systems while advancing the theoretical understanding of legal text processing challenges.