Entity recognition and tagging in legislative texts

Bill drafting and amendments

Italy - Senate

Use case ID: 039

Author: Senate of Italy

Date: 12 June 2024 

Objective:

Automatically identify and tag relevant entities such as people, organizations, locations, dates and legal references within legislative texts, thereby enhancing the efficiency and accuracy of legal document analysis and research.

Actors:

  • Parliamentary staff members
  • Artificial intelligence (AI) development and support team

Prerequisites:

  • Access to a comprehensive dataset of legislative texts
  • Pre-trained language models specialized in legal text
  • Integration with parliamentary document management systems
  • Defined user access permissions and roles

Scenario:

  1. A parliamentary staff member uploads a legislative document to the system.
  2. The AI system processes the document using natural language processing (NLP) techniques.
  3. The AI system identifies and tags relevant entities such as names of legislators, dates, legal references and specific terms.
  4. The tagged document is reviewed by the legal analyst for accuracy.
  5. The document, now enriched with metadata, is stored in the parliamentary document management system.
  6. MPs and other authorized users can search and retrieve documents based on tagged entities, facilitating quicker access to relevant information.

Alternate flows:

  • If the uploaded document format is not supported, the system prompts the user to convert the document into a supported format.
  • In case of ambiguity or low-confidence tags, the system flags these instances for manual review and correction by the legal analyst.

Expected results:

  • Accuracy and speed in legal text analysis are enhanced.
  • Searchability and retrieval of legislative documents are improved.
  • Manual workload for legal analysts is reduced.
  • MPs make better-informed decisions through quick access to relevant information.

Potential challenges:

  • Ensuring the AI system accurately identifies and tags entities in complex legal language
  • Handling variations and updates in legal terminology and references
  • Integrating the AI system seamlessly with existing document management systems
  • Addressing concerns related to data privacy and security

Data requirements:

  • Historical legislative documents for model training
  • Annotations of legal entities for supervised learning
  • Continual updates of legal texts and terminologies to refine the model

Integrations with other systems:

  • Parliamentary document management system
  • Legal databases for cross-referencing
  • User authentication and access control systems

Success metrics:

  • Accuracy rate of entity recognition and tagging
  • Reduction in time taken for legal analysts to annotate documents
  • Increase in the number of successful searches for tagged entities
  • User satisfaction scores from parliamentary staff and researchers

 

The Use cases for AI in parliaments collection is published by the IPU’s Centre for Innovation in Parliament as part of the Parliamentary Data Science Hub’s project to create guidelines for AI governance in parliaments.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International licence. It may be freely shared and reused with acknowledgement of the author and the IPU. 

A use case describes how a system should work. It is used to plan, develop and measure implementation. A use case is not the same as a case study, which is a descriptive text of an actual project’s implementation. Please note that this use case is provided “as is” and neither the IPU nor the author accepts any responsibility for its use.

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