Toward an Intelligent Tutoring System for Argument Mining in Legal Texts
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Abstract (arXiv)
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).
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 8 of its 41 references that are in the map. First posted on arXiv 24 Oct 2022.
Builds on
8 of 41 references.
References · 8
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2019Identification of Rhetorical Roles of Sentences in Indian Legal Judgmentscited by 39
- 2015Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Reviewcited by 13
- 2021Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domaincited by 12
- 2021Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domainscited by 11
- 2021Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documentscited by 9
- 2021Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Datacited by 5
- 2022Data-Centric Machine Learning in the Legal Domaincited by 2
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Citing papers per year: 2023: 1.
1 paper.