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 | LexBot AI — An AI-Based Legal Document Analysis System |
|---|---|
| ABSTRACT | Legal textual data has grown to an unprecedented extent as a result of the ongoing digitization of court docu- ments, statutes, contracts, and case law. It is becoming more and more necessary for lawyers to review large amounts of unstructured documents in short amounts of time, which re- sults in inefficiencies, inconsistent interpretations, and increased mental strain. Contextual dependencies, argumentative struc- ture, and legal reasoning patterns present in judicial texts are not sufficiently captured by conventional keyword-based retrieval systems [1][10]. This study suggests LexBot AI, an integrated artificial intelligence-driven legal document analysis system with named entity recognition, contextual summarization, and question-answering features specific to Indian legal corpora, as a solution to these issues. To efficiently process Indian regional languages, the suggested system makes use of transformer- based architectures like LegalBERT for domain-specific con- textual encoding, BERTSUM for extractive summarization, and multilingual language models like MuRIL and IndicNLPSuite [4][5][15][16]. LexBot AI combines several analytical modules into a single framework, allowing for thorough document in- terpretation in contrast to disjointed solutions that function independently. Improved entity extraction accuracy, cross-lingual adaptability, and summarization coherence are demonstrated by experimental evaluation on Indian court rulings. The results show that modular integration and domain-specific fine-tuning improve the analytical dependability and interpretability of legal AI systems. The study advances context-aware, scalable, and morally sound automation in the legal technology sector. |
| AUTHOR | SAHIL DASHRATH PATIL, ANKUSH ANIL MANE, YOGESH TUKARAM INGAVALE, RUSHIKESH RAJARAM PATIL, RAJVARDHAN UTTAM PATIL, NURAIN AANIS SAYYAD Department of Computer Science, Sanjeevan Group of Institutions, Panhala, Kolhapur, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1406025 |
| pdf/25_LexBot AI — An AI-Based Legal Document Analysis System.pdf | |
| KEYWORDS | |
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