Natural Language Processing for AI-Powered Legal Document Analysis

This work proposes a robust NLP based framework which aims at automated analysis and classification of legal documents using the modern AI products. The methodology proceeds by normalizing text of the legal domain using SpaCy, which normalises domain-specific content and improves the syntactic structure of the content. Attention based mechanisms stemming from transformer models are used for identifying contextually relevant features so that the system does the hard work and pays attention to important legal expressions and phrases. An accurate legal text type classification is done using a fine-tuned LegalBERT classifier with the pretraining based on comprehensive legal corpora. The implementation is conducted by using the Hugging Face Transformers library capable of providing efficient model fine tuning and deployment. Experimental results show that the proposed system has much better accuracy, interpretability and achieves faster training compared with SVM and a general purpose BERT in similar settings. This framework provides a scalable and explainable solution to the legal professionals and institution seeking to reduce legal research, case classification, and contract analysis efforts.