International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Intelligent Legal Document Analysis and Natural Language Query Assistance: An Integrated AI-Driven Framework |
|---|---|
| ABSTRACT | The rapid proliferation of legal documentation across judicial, corporate, and administrative domains has created a significant bottleneck in information retrieval and interpretation. Legal professionals and lay users alike struggle to efficiently extract actionable insights from dense, terminologically complex legal texts. Problem Statement: Conventional document management tools are inadequate for nuanced legal query resolution, lacking contextual awareness, semantic understanding, and the ability to correlate information across heterogeneous document types. Methodology: This paper presents an integrated artificial intelligence framework that combines transformer-based large language models (LLMs) with a retrieval-augmented generation (RAG) pipeline to facilitate intelligent legal document analysis and natural language query assistance. The proposed architecture employs document ingestion via optical character recognition (OCR), semantic chunking, vector embedding using dense retrieval models, and response synthesis via fine-tuned language models. Results: Experimental evaluations demonstrate that the system achieves a query accuracy of approximately 91.4%, with an average response latency of 1.8 seconds and a document processing throughput capable of handling multi-page contracts and statutes. The framework outperforms conventional keyword-based retrieval systems by a margin of 28.6% on semantic relevance metrics. Contributions: The work contributes a novel end-to-end pipeline for legal AI assistance, encompassing domain-specific prompt engineering, hybrid search mechanisms, and an interpretable response interface, thereby democratizing access to legal knowledge for non-specialist users. |
| AUTHOR | ATTILI TEJA NAVADEEP, DR. CHIRAPARAPU SRINIVASA RAO PG Scholar, Department of Computer Science S.V.K.P & Dr. K.S. Raju Arts and Science College (Autonomous), Penugonda, Affiliated to Adikavi Nannaya University, Andhra Pradesh, India Associate Professor, Department of Master of Computer Applications, S.V.K. P & Dr. K. S. Raju Arts and Science College (Autonomous), Penugonda, Affiliated to Adikavi Nannaya University, Andhra Pradesh, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405113 |
| pdf/113_Intelligent Legal Document Analysis and Natural Language Query Assistance An Integrated AI-Driven Framework.pdf | |
| KEYWORDS | |
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