Public Sector — Regulatory Document Entity Extraction and Taxonomy Development

Overview

The purpose of this DAG is to streamline the extraction of named entities (NER) from regulatory documents and to create a comprehensive taxonomy that organizes these entities for better knowledge management. The data sources for this workflow include regulatory documents, legal texts, and compliance guidelines, which are ingested into the system for processing. The ingestion pipeline utilizes natural language processing (NLP) techniques to identify key entities such as organizations, dates, and

The purpose of this DAG is to streamline the extraction of named entities (NER) from regulatory documents and to create a comprehensive taxonomy that organizes these entities for better knowledge management. The data sources for this workflow include regulatory documents, legal texts, and compliance guidelines, which are ingested into the system for processing. The ingestion pipeline utilizes natural language processing (NLP) techniques to identify key entities such as organizations, dates, and legal references. Processing steps include entity recognition, validation of extracted entities, and classification according to a predefined taxonomy. Quality controls are implemented to ensure the accuracy of entity extraction, with metrics such as precision rates and taxonomy update times monitored as key performance indicators (KPIs). The outputs of this DAG consist of a validated list of entities and an updated taxonomy, which can be integrated into a knowledge management system for easy access and retrieval. The business value lies in improving regulatory compliance, enhancing the efficiency of document management, and facilitating informed decision-making within the public sector. In the event of extraction failures, a reevaluation process is initiated to ensure continuous improvement and accuracy.

Part of the Literature Review solution for the Public Sector industry.

Use cases

• Improves regulatory compliance through accurate information retrieval
• Enhances efficiency in managing large volumes of regulatory documents
• Facilitates informed decision-making with structured data access
• Reduces manual effort in entity extraction and classification
• Supports continuous improvement through monitoring and reevaluation