Information extraction framework to build legislation network
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 in order to extract network information. The study highlights the importance of data accuracy in network analysis and improves approximate string matching techniques to produce reliable network data sets with more than 98% precision and recall. The applications and the complexity of the created dynamic legislation network are also discussed and challenged.
Artificial Intelligence and Law