ADVOMATE -When Justice Meets Technology
Abstract
Advomate – When Justice Meets Technology is an innovative web-based platform designed to simplify and enhance access to legal information and services through modern technology. The primary goal of this system is to bridge the gap between individuals and legal knowledge by providing a user-friendly interface where users can explore various domains of law such as criminal law, civil law, banking and finance law, family law, cyber law, and more.The application features a secure login system that ensures personalized access for users. Once authenticated, users are directed to a dashboard that serves as a centralized hub for navigating different legal categories. Each section provides structured information, helping users understand legal concepts, rights, and procedures in a simplified manner.Advomate is built using modern web technologies, with a responsive frontend developed using HTML, CSS, and JavaScript, and a robust backend powered by MongoDB for efficient data storage and management. The platform emphasizes accessibility, usability, and scalability, making it suitable for a wide range of users including students, legal professionals, and the general public.By integrating technology with legal awareness, Advomate aims to empower individuals with knowledge, promote legal literacy, and make justice more approachable and transparent in the digital age.
References
D. Jurafsky and J. H. Martin, Speech and Language Processing, 2nd ed. Upper Saddle River, NJ, USA: Prentice Hall, 2008.
C. D. Manning and H. Schütze, Foundations of Statistical Natural Language Processing. Cambridge, MA, USA: MIT Press, 1999.
C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer, 2006.
A. Sharma et al., “Legal Text Classification Using Machine Learning and Deep Learning Techniques,” Information Processing & Management, vol. 59, no. 2, 2022.
R. Bakker et al., “Semantic Role Extraction in Legal Documents Using Language Models,” Artificial Intelligence and Law, Springer, 2025.
K. Mishra, H. Pagare, and K. Sharma, “Hybrid NLP and Machine Learning Approach for Legal Document Processing,” Scientific Reports, 2025.
S. Verma et al., “Legal Document Summarization Using Classification and NLP Techniques,” International Journal of Data Warehousing and Mining, 2025.
P. Gupta et al., “Transformer-Based Legal Named Entity Recognition (LegNER),” Frontiers in Artificial Intelligence, 2025.
M. Rao et al., “Recent Trends in Legal AI and NLP Applications,” Proceedings of ICAISS Conference, IEEE, 2025.
“Proceedings of the Workshop on NLP for Empowering Justice (JUST-NLP),” ACL Anthology, 2025.
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