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 le...

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Bibliographic Details
Published inArtificial intelligence and law Vol. 29; no. 1; pp. 35 - 58
Main Authors Sakhaee, Neda, Wilson, Mark C.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.03.2021
Springer
Springer Nature B.V
Subjects
Approximate string matching
Artificial Intelligence
Computer Science
Information retrieval
Information Storage and Retrieval
Intellectual Property
IT Law
Legal Aspects of Computing
Legal documents
Legislation
Media Law
Network analysis
Original Research
Philosophy of Law
Relation extraction
Evaluation
Approximate string matching
Optical character recognition
Legislation network
Named entity recognition
Information extraction
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Summary: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.
ISSN:0924-8463
1572-8382
DOI:10.1007/s10506-020-09263-3