# Information Extraction Framework to Build Legislation Network

## TL;DR

This paper presents an information extraction framework that constructs a dynamic legislation network from legal texts, emphasizing data accuracy and improved string matching to achieve over 98% precision and recall.

## Contribution

It introduces a novel extraction methodology that avoids supervised learning, enhancing the reliability of legislation networks from legal documents.

## Key findings

- Achieved over 98% precision and recall in data extraction
- Enhanced approximate string matching techniques
- Discussed the complexity and applications of the legislation network

## Abstract

This paper concerns an Information Extraction process for building a dynamic Legislation Network from legal documents. Unlike supervised learning approaches which require additional calculations, the idea here is to apply Information Extraction methodologies by identifying distinct expressions in legal text and extract quality network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques for producing reliable network data-sets with more than 98 percent precision and recall. The values, applications, and the complexity of the created dynamic Legislation Network are also discussed and challenged.