Title
Legal Markup Generation in the Large: An Experience Report
Abstract
Legal markup (metadata) is an important prerequisite for the elaboration of legal requirements. Manually encoding legal texts into a markup representation is laborious, specially for large legal corpora amassed over decades and centuries. At the same time, automating the generation of markup in a fully accurate manner presents a challenge due to the flexibility of the natural-language content in legal texts and variations in how these texts are organized. Following an action research method, we successfully collaborated with the Government of Luxembourg in transitioning five major legislative codes from plain-text to a legal markup format. Our work focused on generating markup for the structural elements of the underlying codes. The technical basis for our work is an adaptation and enhancement of an academic markup generation tool developed in our prior research [1]. We reflect on the experience gained from applying automated markup generation at large scales. In particular, we elaborate the decisions we made in order to strike a cost-effective balance between automation and manual work for legal markup generation. We evaluate the quality of automatically-generated structural markup in real-world conditions and subject to the practical considerations of our collaborating partner.
Year
DOI
Venue
2017
10.1109/RE.2017.10
2017 IEEE 25th International Requirements Engineering Conference (RE)
Keywords
Field
DocType
Legal Requirements,Legal Markup,Natural Language Processing (NLP)
Metadata,World Wide Web,Collaborative Application Markup Language,Document Definition Markup Language,XML,Software engineering,Computer science,XHTML,Action research,Management science,RuleML,Markup language
Conference
ISSN
ISBN
Citations 
2332-6441
978-1-5386-3192-8
1
PageRank 
References 
Authors
0.37
8
7
Name
Order
Citations
PageRank
Nicolas Sannier1887.77
Morayo Adedjouma2326.16
Mehrdad Sabetzadeh398861.84
Lionel C. Briand48795481.98
John Dann571.80
Marc Hisette610.37
Pascal Thill710.37