Foundations of statistical natural language processing
Foundations of statistical natural language processing
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
Automatic semantics extraction in law documents
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Is linguistic information relevant for the classification of legal texts?
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Automated Detection of Reference Structures in Law
Proceedings of the 2006 conference on Legal Knowledge and Information Systems: JURIX 2006: The Nineteenth Annual Conference
Automatic Classification of Sentences in Dutch Laws
Proceedings of the 2008 conference on Legal Knowledge and Information Systems: JURIX 2008: The Twenty-First Annual Conference
Proceedings of the 2007 conference on Legal Knowledge and Information Systems: JURIX 2007: The Twentieth Annual Conference
Machine Learning versus Knowledge Based Classification of Legal Texts
Proceedings of the 2010 conference on Legal Knowledge and Information Systems: JURIX 2010: The Twenty-Third Annual Conference
Automated classification of norms in sources of law
Semantic Processing of Legal Texts
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The ultimate goal of the research line described here is support for automated modelling of sources of law. One of the first steps is the automatic recognition of norms. In earlier work we presented a categorization of norms or provisions in legislation. We claimed that the categories are characterized by the use of typical sentence structures and that this would enable automatic detection and classification. In this paper we present the results of experiments in such automatic classification of provisions. We have defined fourteen different categories of provisions, and compiled a list of 88 sentence structures for those categories from twenty Dutch laws. Based on these structures, a parser was used to classify the sentences in fifteen different Dutch laws, classifying 91% of 592 sentences correctly. It compares well with other, statistical approaches. An important improvement of our classifier will be the distinction of principal and auxiliary sentences.