Toward an Intelligent Tutoring System for Argument Mining in Legal Texts

Autor:	Westermann, Hannes, Savelka, Jaromir, Walker, Vern R., Ashley, Kevin D., Benyekhlef, Karim
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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). Comment: Accepted for presentation at the 35th International Conference on Legal Knowledge and Information Systems (JURIX 2022) and publication in the Frontiers of Artificial Intelligence and Applications series of IOS Press
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.13635 Zobrazit plný text záznamu View this record from Arxiv