Completed
Legal Case Outcome Prediction with NLP
Transformer-based models for automated legal analysis
As a Machine Learning Intern at the TUKL Deep Learning Lab, I developed an end-to-end machine learning pipeline for predicting court case outcomes using state-of-the-art natural language processing techniques.
The Challenge of Legal Text Processing
Legal documents present unique challenges for machine learning systems. They contain highly specialized terminology, complex sentence structures, and domain-specific reasoning patterns.
Automated Data Pipeline
I developed a comprehensive Python pipeline that automated the extraction, structuring, and preprocessing of data from raw court documents:
- Document parsing and segmentation
- Named entity recognition for parties, judges, and legal entities
- Citation extraction and linking
- Text normalization while preserving legal terminology
- Feature engineering specific to legal reasoning patterns
Transformer-Based Model Architecture
The core predictive system utilized Transformer-based NLP models implemented in both TensorFlow and PyTorch. Fine-tuning involved:
- Domain adaptation through continued pre-training on legal corpora
- Task-specific training on labeled case outcomes
- Hyperparameter optimization for legal text characteristics
- Regularization techniques to prevent overfitting
Model Performance
The fine-tuned models achieved 83% accuracy on the custom legal case dataset with strong generalization across different case types and legal jurisdictions.