Predicting the Duration of Judicial Cases Using Hybrid Systems Based on Language Models

Recent technological developments in the field of Natural Language Processing (NLP), notably due to Transformer architectures and language models, have made it possible to tackle aspects that were previously inaccessible with traditional tools. The present study addresses the issue of predicting legal case durations using Arabic judicial data. For this task, hybrid systems based on language models were implemented. The Arabic_LegalBERT model, derived from AraBERT and specialized through additional pre-training on an Arabic legal corpus, was proposed to generate representations that were integrated into the downstream steps of the approach. Two methods were adopted for predicting the processing time of a new case: The first followed a framework combining automatic classification with statistical correspondence, while the second relied on cosine similarity combined with empirical statistics. The results obtained with the classification approach are particularly promising, with a small improvement for the system based on the specialized model. For the similarity-based approach, the results are also promising, with a clear distinction observed when evaluating each type individually, indicating that types with a higher number of cases generally perform better than those with fewer cases.

@article{BOUHOUCHE2025,
  title     = {Predicting the Duration of Judicial Cases Using Hybrid Systems Based on Language Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Amina BOUHOUCHE and Saliha YASSINE and Mustapha ESGHIR and Mohammed ERRACHID},
  doi       = {10.14569/IJACSA.2025.0161270},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161270}
}