Rule-based Classifier Models

TL;DR

This paper extends classifier models in the legal domain by integrating rules and legal reasoning, enabling more nuanced case classification that considers facts, rules, and hierarchical court structures.

Contribution

It introduces a novel rule-based classifier framework that incorporates legal rules, reasoning, and hierarchical court information into case classification models.

Findings

Demonstrates how decisions can be inferred using the new rule-based classifier.

Shows integration of time elements and court hierarchy into the classifier.

Builds on existing work to enhance legal case analysis.

Abstract

We extend the formal framework of classifier models used in the legal domain. While the existing classifier framework characterises cases solely through the facts involved, legal reasoning fundamentally relies on both facts and rules, particularly the ratio decidendi. This paper presents an initial approach to incorporating sets of rules within a classifier. Our work is built on the work of Canavotto et al. (2023), which has developed the rule-based reason model of precedential constraint within a hierarchy of factors. We demonstrate how decisions for new cases can be inferred using this enriched rule-based classifier framework. Additionally, we provide an example of how the time element and the hierarchy of courts can be used in the new classifier framework.