Toward an Intelligent Tutoring System for Argument Mining in Legal Texts

EasyChair Preprint 9169

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).

Keyphrases: Human Computer Interaction, Intelligent Tutoring System, Legal text classification, argument mining, case brief, caselaw analysis, legal annotation, legal education

@booklet{EasyChair:9169,
  author    = {Hannes Westermann and Jaromir Savelka and Vern R. Walker and Kevin D. Ashley and Karim Benyekhlef},
  title     = {Toward an Intelligent Tutoring System for Argument Mining in Legal Texts},
  howpublished = {EasyChair Preprint 9169},
  year      = {EasyChair, 2022}}