Legal Text Analytics for Reasonable Notice Period Prediction
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Applications of deep learning (DL) to generate text embeddings and natural language processing (NLP) have shown wide success in semantic interpretations of domain-specific text data when applied to downstream tasks such as predicting the next word, information extraction for classification, analyzing social media feeds, classifying text, and creating compressed representations. While DL and NLP have been widely applied across numerous domains, researchers have recently begun to apply these techniques to the field of law due to the challenges in processing legal case descriptions. Attention-based models have shown promising results in predicting criminal charges using unstructured text as an input, but little work has been done on data representing the Canadian legal system, especially employment law. The legal field poses many challenges, such as the amount of legal data publicly available in Canada, the verbosity of judgments, the legal jargon used in judgments, and the subjectivity of outcomes that pose many challenges in processing legal text data. Many of the state-of-the-art systems require expensive hand-annotated labels that are often unobtainable. In this study, we investigate the prediction of reasonable notice for termination of employment in the field of law. To address these challenges, we propose domain-adapted BERT variations specifically trained for legal texts. We assess the performance of various attention-based and pre-trained models using human-typed summaries of legal judgment and present a detailed analysis of the data and the results to provide insights for further exploration in this area. Our approaches also provide interesting insights about this specific type of legal case focusing on employment law, given the subjective nature of judges and the variability in outcomes. Received: 15 August 2024 | Revised: 28 February 2025 | Accepted: 10 March 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available at https://static.case.law/. Author Contribution Statement Jason Lam: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Yuhao Chen: Writing – review & editing, Visualization. Farhana Zulkernine: Conceptualization, Resources, Data curation, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Samuel Dahan: Validation, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle