Toward an Intelligent Tutoring System for Argument Mining in Legal Texts

TL;DR

This paper introduces CABINET, an adaptive intelligent tutoring system for legal argument mining that leverages a novel cognitive computing framework to assist law students and professionals in caselaw analysis.

Contribution

It presents a new cognitive computing framework tailored for legal argument mining, integrating machine learning capabilities to adapt to user proficiency levels.

Findings

System can identify analysis errors with 2.0-3.5% false positive rate.

Predicts key argument elements with an F1-score of 0.74.

Feasibility of the framework demonstrated through promising experimental results.

Abstract

We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our experiments focused on the feasibility of the proposed framework are promising. We show that the system is capable of identifying a potential error in the analysis with very low false positives rate (2.0-3.5%), as well as of predicting the key argument element type (e.g., an issue or a holding) with a reasonably high F1-score (0.74).