This paper describes an approach at Named Entity Recognition (NER) in German language documents from the legal domain. For this purpose, a dataset consisting of German court decisions was developed. The source texts were manually annotated with the following 19 semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. Overall, the dataset consists of approximately 67,000 sentences and contains around 54,000 annotated entities.
DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz
https://www.dfki.de/
DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz
https://www.dfki.de/
DFKI - Deutsches Forschungszentrum für Künstliche Intelligenz
https://www.dfki.de/
Open Government — the right for citizens to access governmental information — has a long tradition in the Netherlands. Recent Dutch legislation requires government agencies to publish their data electronically, so that citizens can subscribe to information of their interest by e-mail. In order to facilitate this, we present a solution where we enrich government publication with knowledge graphs based on RDF/OWL. In this way, we can deal with the considerable variation in the ways that local governmental agencies enrich their data and publications.
Dr.
As software engineer at Taxonic Guido van der Wolk is responsible for developing semantic-driven applications. After obtaining a PhD in astronomy, Guido worked on IT solutions for several clients in commerce, industry and government. He is specialized in model-driven development, data science and machine learning.
