  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.
