期刊
ARTIFICIAL INTELLIGENCE AND LAW
卷 29, 期 2, 页码 171-211出版社
SPRINGER
DOI: 10.1007/s10506-020-09271-3
关键词
Classification; Information extraction; Ontology; Normative reasoning; Semantic role labeling; Artificial intelligence; Law
This article introduces a semantic role labeling based information extraction system to extract definitions and norms from legislation and represent them as structured norms in legal ontologies. The goal is to make laws more accessible, understandable, and searchable in a legal document management system.
This article seeks to address the problem of the 'resource consumption bottleneck' of creating legal semantic technologies manually. It describes a semantic role labeling based information extraction system to extract definitions and norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system.
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