# Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts

arXiv: https://arxiv.org/abs/2604.17674 · HTML: https://mlsanity.com/paper/2604.17674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization](https://mlsanity.com/paper/2502.20364/) (2025)
- [A Survey of Classification Tasks and Approaches for Legal Contracts](https://mlsanity.com/paper/2507.21108/) (2025)
- [NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System](https://mlsanity.com/paper/2508.00709/) (2025)
- [Topic Classification of Case Law Using a Large Language Model and a New Taxonomy for UK Law: AI Insights into Summary Judgment](https://mlsanity.com/paper/2405.12910/) (2024)
- [Tasks and Roles in Legal AI: Data Curation, Annotation, and Verification](https://mlsanity.com/paper/2504.01349/) (2025)
- [RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models](https://mlsanity.com/paper/2505.21281/) (2025)
- [Can Large Language Models Predict the Outcome of Judicial Decisions?](https://mlsanity.com/paper/2501.09768/) (2025)
- [Identifying Legal Holdings with LLMs: A Systematic Study of Performance, Scale, and Memorization](https://mlsanity.com/paper/2505.02172/) (2025)
- [Universal Legal Article Prediction via Tight Collaboration between Supervised Classification Model and LLM](https://mlsanity.com/paper/2509.22119/) (2025)

## Abstract (arXiv)

Legal practitioners and judicial institutions face an ever-growing volume of case-law documents characterised by formalised language, lengthy sentence structures, and highly specialised terminology, making manual triage both time-consuming and error-prone. This work presents a lightweight yet high-accuracy framework for citation-treatment classification that pairs lemmatisation-based preprocessing with subword-aware FastText embeddings and a multi-kernel one-dimensional Convolutional Neural Network (CNN). Evaluated on a publicly available corpus of 25,000 annotated legal documents with a 75/25 training-test partition, the proposed system achieves 97.26% classification accuracy and a macro F1-score of 96.82%, surpassing established baselines including fine-tuned BERT, Long Short-Term Memory (LSTM) with FastText, CNN with random embeddings, and a Term Frequency-Inverse Document Frequency (TF-IDF) k-Nearest Neighbour (KNN) classifier. The model also attains the highest Area Under the Receiver Operating Characteristic (AUC-ROC) curve of 97.83% among all compared systems while operating with only 5.1 million parameters and an inference latency of 0.31 ms per document - more than 13 times faster than BERT. Ablation experiments confirm the individual contribution of each pipeline component, and the confusion matrix reveals that residual errors are confined to semantically adjacent citation categories. These findings indicate that carefully designed convolutional architectures represent a scalable, resource-efficient alternative to heavyweight transformers for intelligent legal document analysis.