Automated classification of norms in sources of law

The research described here attempts to achieve automated support for modelling sources of law for legal knowledge based systems and services. Many existing systems use models that do not reflect the entire law, and simplify parts of the text. These models are difficult to validate, maintain and re-use. We propose to create an intermediate model that has an isomorphic representation of the structure of the original text. A first step towards automated modelling is the detection and classification of provisions in sources of law. A list of different categories of norms and provisions that are used in Dutch legal texts is presented. These categories can be identified by the use of typical text patterns. Next, the results of experiments in automated classification of provisions using these patterns are presented. 91% of 592 sentences in fifteen different Dutch laws were classified correctly. Some conclusions about the generality of the approach are drawn and future research is outlined.