A sequence approach to case outcome detection

Abstract

We describe a system to detect the outcome of U.S. Federal District Court cases based on PACER electronic dockets. We study the text processing components of the system and develop two model architectures in order to detect the outcome of a case per party (e.g., dismissed by Court or Verdict for Plaintiff). We conclude that modeling cases as a linear-chain graphical model (i.e., Conditional Random Field (CRF)) offers significantly better performance than modeling the case entry-by-entry (i.e., Logistic Regression (LR)). We in particular show that a first-order modeling of the CRF significantly outperforms the factorized model for the CRF architecture.