FAQ

Advancing Regulatory and Standards Publishing: The ML and NLP Revolution

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Organizations that publish rules, regulations or standards are increasingly looking to streamline their processes while enhancing the usability of their content. Here, we answer some frequently asked questions about how Machine Learning (ML) and Natural Language Processing (NLP) can benefit publishers of standards, regulations and rules.

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ML and NLP FAQs

How can ML improve regulatory document management?

ML can analyse large datasets to categorize documents or document fragments based on their content. ML can assign relevant tags or metadata to text by understanding context and intent providing more relevant results than simple keyword matching. In the context of regulation and standards creation, ML can improve overflow editorial workflow efficiency for review and approval cycles by analysing document content to identify potential risks or liabilities. Predictive analytics models can analyse historical data to forecast the potential impact of proposed regulations and standards. Additionally, ML can match documents with the most suitable reviewers, optimizing the review process and ensuring timely and thorough evaluations.

What is NLP and how does it apply to organisations who create standards and regulations?

NLP is a branch of artificial intelligence that focuses on the interaction between computers and human language. For organizations that create standards, NLP can be used for common tasks like text analysis, language simplification, multilingual support and extracting keyword information. NLP can also be used for consistency checking across the organisations publications to ensure consistent terminology, formatting and structure of different standards and regulations.

What are the benefits of automating regulatory reporting with NLP and ML?

Automating regulatory reporting with ML and NLP can reduce the manual workload on standards and regulatory teams. NLP can generate accurate and compliant reports by extracting and summarizing relevant information from regulatory documents. ML can further enhance this process by identifying trends and insights that may be useful for strategic decision-making. Automation ensures timely and accurate reporting as regulations change, freeing up resources for other critical tasks.

How can organisations get started with NLP and ML in standards and regulatory publishing?

Organisations can start by assessing their current publishing processes to identify target areas where ML and NLP can add value. Successful implementations require a combination of technical expertise, domain knowledge and change management - this is where GPSL can help.

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Find our more about ML and NLP in standards and regulations publishing

Get in touch with our AI team today to discuss how our domain expertise and industry specific knowledge can transform your publishing processes.