Citation

Jayaram, Jayapradha and Haw, Su Cheng and Palanichamy, Naveen and Bhattacharya, Nilanjana and Agarwal, Aayushi and Thillaigovindhan, Senthil Kumar (2025) CodifiedCant: Enhancing Legal Document Accessibility Using NLP and Longformer for Secure and Efficient Compliance. International Journal of Advanced Computer Science and Applications, 16 (5). ISSN 2158107X

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Abstract

CodifiedCant is a new idea that employs Natural Language Processing to simplify company guidelines and legal documents. Legal texts are extensive, complicated and hard for non-experts to understand. To tackle the above problem, this research incorporates the Longformer model because it functions as a transformer-based deep learning system designed to work effectively with extensive legal documents. Longformer enables the system to handle extensive documents by keeping better track of context, which results in transforming complex legal text into easily readable formats. To enhance the search and retrieval speed, this research investigates the nuances of transforming unstructured data, like tabular data from PDFs, to vectors. This revolution supports quicker, cognisant semantic routing inside the document. Further, it assists in data arrangement and detection across massive sources of legitimate and business information. Data security is also a major priority for the platform, which utilizes network encryption to protect data and privacy. CodifiedCant is a scalable, secure and intelligent solution for better employee access to legal news, greater company transparency and reinforces better compliance in the organization. Table extraction and document simplification performance of the model are validated on Cornell LII and Kaggle evaluation datasets, respectively. CodifiedCant associates the variance relating to legitimate terminology and user knowledge.

Item Type:	Article
Uncontrolled Keywords:	transformer-based deep learning system
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	26 Jun 2025 08:06
Last Modified:	26 Jun 2025 08:06
URII:	http://shdl.mmu.edu.my/id/eprint/14123

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