This paper reports on an approach to automatically transform semi-structured and public databases of US state-level legislative bills into a structured, legal corpus, namely the Corpus of US Bills (CoUSBi). Our work has resulted in a methodology and a corpus that makes this data usable for natural language processing applications. It thus also lays important groundwork for work in the social sciences, particularly in the fields of political science and economics where there is a growing interest in the relationship between legislative policy-making and economic behavior. Against the backdrop of eventually contributing to a Legal Knowledge Graph, the paper shows that the corpus we provide already fulfills the requirements to be connected to other resources: We automatically extract correspondences between individual state bills and model bills from independent organizations, generating interesting insights into the legislative process. We furthermore use NEREx, a Visual Analytics framework, that allows us to capture important content of the bills at a glance.
@InProceedings{KALOULI18.5, author = {Aikaterini-Lida Kalouli ,Leo Vrana ,Vigile Marie Fabella ,Luna Bellani and Annette Hautli-Janisz}, title = {CoUSBi: A Structured and Visualized Legal Corpus of US State Bills }, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Georg Rehm and Víctor Rodríguez-Doncel and Julián Moreno-Schneider}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-18-4}, language = {english} }
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