This project aims to automate the process of legal text classification by applying Machine Learning (ML) and Natural Language Processing (NLP) techniques.
We have implemented Random Forest, LSTM, and Stacked Ensemble models to accurately categorize legal documents into their respective classes.
In the legal domain, vast amounts of documents — judgments, legislation, and case files — are generated daily.
Organizing and retrieving these documents efficiently is a major challenge for lawyers, judges, and legal firms.
Manual review is not only time-intensive but prone to human error.
This project addresses this bottleneck by designing a pipeline to automate the process of legal text classification with high accuracy.
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We used a custom legal text dataset with 7 classes:
- CITED
- APPLIED
- REFERRED
- FOLLOWED
- RELATED
- DISCUSSED
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The dataset was imbalanced, with some classes underrepresented.
To maximize the classifier’s ability to learn from raw text, we performed extensive text cleansing and transformation:
✅ Text Tokenization
✅ Removal of Stop Words (with NLTK’s standard list)
✅ Lemmatization (with WordNet Lemmatizer) — reducing words to their base forms (running → run)
✅ Lowercasing — for uniform representation
✅ Handling of Class Imbalance (with SMOTE or ADASYN) — to produce a more balanced training set.
Such careful text processing helps to reduce noise, standardize inputs, and enable our models to learn more robust patterns.
We implemented 3 different models:
➥ Random Forest (with TF-IDF vectorization)
Provides strong baseline performance with interpretability.
➥ LSTM (with Embedding Layers)
Leverages semantic relationships in text — especially helpful for context-dependent phrases in legal documents.
➥ Stacked Ensemble
Combination of both the above to produce more robust and accurate predictions.
To account for class imbalance and to gauge true performance across all classes, we used:
✅ Accuracy
✅ Precision
✅ Recall
✅ F1-Score (with per-class metrics)
✅ Confusion Matrix (with per-label breakdown)
This lets us identify which classes perform well and where we need further improvement.
Class imbalance can undermine classifier performance — causing it to be biased toward majority classes.
To address this:
✅ We used SMOTE (Synthetic Minority Oversampling) to generate additional samples for underrepresented classes.
✅ This resulted in a much more balanced training set, which directly improved classifier performance across all classes.
✅ The F1 score for previously underrepresented classes improved by up to 20%.
Originally, with imbalanced classes, we were seeing an accuracy of roughly 75% with poor F1 scores for many classes.
After applying SMOTE and fine-tuning hyperparameters (with grid search) for both the LSTM and the Random Forest, and then employing stacked ensembling, we:
✅ Increased the accuracy to 90%+
✅ F1 score for each class surpassed 0.85
✅ Overall robustness and generalization improved — reducing overfitting.
✅ Text Tokenization ✅ Stop Words Removal ✅ Lemmatization ✅ Text Normalization ✅ SMOTE Oversampling ✅ TF-IDF Vectorization ✅ Embedding Layers with LSTM ✅ Stacked Model for Better Performance ✅ Detailed Evaluation with F1, Accuracy, and Classification Report per class
- Python 3.x
- Scikit-learn
- TensorFlow/Keras (for LSTM)
- NLTK (for text processing)
- Pandas, NumPy, and Matplotlib (for data manipulations)
This pipeline successfully demonstrates the power of Machine Learning and NLP in automating legal document classification — even under challenging conditions stemming from class imbalance.
Through careful data preparation, algorithm selection, and evaluation, we have significantly improved both the accuracy and robustness of the classifier.
✅ Experiment with Transformer models (BERT, RoBERTa) for even greater accuracy.
✅ Implement active learning to aid human reviewers in improving the training set.
✅ Integrate a REST API for real-world application.
✅ Develop a web application with Streamlit for easy and interactive usage.