Legal Text Analytics for Reasonable Notice Period Prediction

作者：Jason Lam, Yuhao Chen, Farhana Zulkernine, Samuel Dahan · 发表于：Journal of Computational and Cognitive Engineering · 年份：2025 · DOI：10.47852/bonviewjcce52024104 · 被引用次数：4 · 研究领域：Artificial Intelligence in Law、Topic Modeling、Computational and Text Analysis Methods

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 summar...