Unified Text Segmentation (TSEG) Deep Learning Function for Legal Text Recovery: Enhancing Generative-NLP and AI-Driven Regulatory Operations in Industry 5.0

Pascal Muam Mah and Tomasz Pelech-Pilichowski

European Research Studies Journal, 2025, vol. XXVIII, issue 2, 1018-1048

Abstract: Purpose: The emergence of Industry 5.0 demands intelligent systems that not only automate tasks but also collaborate meaningfully with humans, especially in highly regulated fields like law and governance. This study addresses the challenges of fragmented, redacted, and corrupted legal documents, which hinder compliance, auditing, and interpretability.. Design/Methodology/Approach: This research introduces a Unified Tseg Deep Learning Function—a multi-layered framework combining legal text segmentation (Tseg), generative NLP techniques, and legal ontology alignment. The model incorporates transformer architectures, attention mechanisms, and recursive state modeling to enable dynamic content recovery, contextual classification, and visibility-based annotation based on age and jurisdiction. Findings: The proposed system effectively recovers missing or corrupted legal content, categorizes segments by legal domain, and applies jurisdiction-specific and age-based visibility rules. It demonstrates strong potential in automating and enhancing interpretability within legal workflows and regulatory compliance systems. Practical Implications: This framework can support digital governance platforms, legal information retrieval systems, and compliance monitoring tools by enabling more accurate and automated reconstruction and classification of legal documents, thus improving transparency and accountability in legal processes Originality/Value: The study offers a novel integration of Tseg, generative NLP, and legal ontologies tailored for Industry 5.0. Its unique contribution lies in bridging technical deep learning methods with domain-specific legal compliance requirements, supporting the evolution of intelligent, human-centric regulatory technologies.

Keywords: Legal Text Recovery; Unified Tseg; natural language processing; Artificial intelligence; Deep Learning; Industry 5.0; Name Entity Recognition. (search for similar items in EconPapers)
JEL-codes: C33 G11 G14 G34 L25 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ersj.eu/journal/4024/download (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ers:journl:v:xxviii:y:2025:i:2:p:1018-1048

Access Statistics for this article

More articles in European Research Studies Journal from European Research Studies Journal
Bibliographic data for series maintained by Marios Agiomavritis ().