Analysis of Legal Documents via Non-negative Matrix Factorization Methods

TL;DR

This paper applies Non-negative Matrix Factorization (NMF) to analyze legal documents from the California Innocence Project, uncovering underlying topics and classifying case files to aid legal review processes.

Contribution

It introduces the application of NMF and its variants to legal document analysis, providing insights into their effectiveness and limitations in a real-world legal context.

Findings

NMF successfully identified underlying topics in case files.

Classified request files by crime type and case status.

Discussed benefits and drawbacks of NMF variants.

Abstract

The California Innocence Project (CIP), a clinical law school program aiming to free wrongfully convicted prisoners, evaluates thousands of mails containing new requests for assistance and corresponding case files. Processing and interpreting this large amount of information presents a significant challenge for CIP officials, which can be successfully aided by topic modeling techniques.In this paper, we apply Non-negative Matrix Factorization (NMF) method and implement various offshoots of it to the important and previously unstudied data set compiled by CIP. We identify underlying topics of existing case files and classify request files by crime type and case status (decision type). The results uncover the semantic structure of current case files and can provide CIP officials with a general understanding of newly received case files before further examinations. We also provide an exposition of popular variants of NMF with their experimental results and discuss the benefits and drawbacks of each variant through the real-world application.