Hybrid CNN: An Empirical Analysis of Machine Learning Models for Predicting Legal Judgments

Artificial Intelligence with NLP has revolutionized the legal industry, which was previously under-digitized, and it's eager to adopt digital technologies for increased efficiency. Case backlog issues, exacerbated by population growth, can be alleviated by AI's potential in decision prediction for laypeople, litigants, and adjudicators. Legal judgment prediction (LJP) is viewed as a text classification cum prediction problem, with encoding models crucial for accurate textual representation and downstream tasks. These models capture syntax, semantics, and context, varying in performance based on the task and dataset. Selecting the right model, whether traditional ML or DL, using different evaluation metrics, is complex. This paper addresses the above research gap by reviewing 12 cutting-edge ML models and 10 DL models with two embedding methods on real-time Madras High Court criminal cases from Manupatra. The comprehensive comparison of classifier models on real-time case documents provides insights for researchers to innovate despite challenges and limitations. Evaluation metrics like accuracy, F1 score, precision, and recall show that Support Vector Machines (SVM), Logistic Regression, and SGD with Doc2Vec (D2V) encoding and shallow neural networks perform well. Although Transformers process longer input sequences with parallel word analysis and self-attention layers, they have weaknesses on real-time datasets. This article proposes a novel hybrid CNN with a transformer model to predict binary judgments, outperforming traditional ML and DL models in precision, recall, and accuracy. Finally, we summarise the most important ramifications, potential research avenues, and difficulties facing the legal research field.

@article{Sukanya2024,
  title     = {Hybrid CNN: An Empirical Analysis of Machine Learning Models for Predicting Legal Judgments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {G. Sukanya and J. Priyadarshini},
  doi       = {10.14569/IJACSA.2024.01507124},
  url       = {https://doi.org/10.14569/IJACSA.2024.01507124}
}