Computational Legal Linguistics

Quantifying strategic shifts in legal discourse through linguistic networks and LLM adoption analysis

Overview

This research program investigates the intersection of computational linguistics, network science, and legal technology through two distinct but complementary projects. The overarching goal is to quantify strategic shifts in legal discourse and the evolving role of automation in professional practice.

Key Contributions

  • Linguistic network modeling: Developed a novel framework for modeling legal filings as evolving linguistic networks using certainty markers and Subject-Verb-Object (SVO) structures
  • Structural Balance Theory application: Applied network-based methodology to detect strategic negotiation postures that precede formal procedural signals
  • LLM adoption tracking: Created a multi-dimensional detection suite to identify generative AI signatures in legal correspondence
  • Socio-technical analysis: Investigated drivers of AI adoption across different firm types and filing categories

Methodology

The research consists of two complementary approaches:

  1. Linguistic Networks in Litigation: Legal filings are modeled as evolving linguistic networks by analyzing:
    • Temporal dynamics of certainty markers
    • Subject-Verb-Object (SVO) structure patterns
    • Triadic configuration transitions from “unbalanced” (adversarial tension) to “balanced” (stabilization) states
  2. LLM Adoption Detection: An interrupted time-series design tracks generative signatures using:
    • Probabilistic measures (Perplexity, Burstiness)
    • Advanced zero-shot classifiers (Binoculars)
    • Pattern analysis across firm types and filing categories

Results

The linguistic network analysis tests whether balance theory transitions serve as reliable lead indicators for case settlement versus trial outcomes. The LLM adoption study provides empirical evidence on how generative AI is reshaping the stylometry of professional legal communication, with hypothesized higher initial penetration in resource-constrained firms and routine procedural filings.

Technologies

  • Python
  • Network Science
  • NLP / Computational Linguistics
  • Time-Series Analysis
  • Zero-shot Classification

Publication

Work in progress