Suggesting Model Fragments for Sentences in Dutch Law

AutoreEmile de Maat - Radboud Winkels
CaricaResearcher at the 'Leibniz Center for Law' (LCL) - Associate Professor in Computer Science and Law at LCL of the Faculty of Law of the University of Amsterdam.
Suggesting Model Fragments for Sentences in Dutch Law
SUMM ARY:1. Introduction – 1.1. General Approach – 2. Sentences Dealing with the
Law – 2.1. Scope Declaration – 2.2. Repeal – 2.3. Insertion – 2.4. Replacement –
2.5. Renumbering 2.6. Enactment Date 2.7. Citation Title 2.8. Application
Provision – 3. Sentences Dealing with Subject Matter – 3.1. Normative Sentences –
3.2. Def‌initions and Deeming Provisions – 4. Experiences – 5. Conclusions
A main issue in the f‌ield of artif‌icial intelligence and law is the trans-
formation of sources of law that are written in natural language (and there-
fore rather informal) into formal models of law that computers can reason
with. This is a time and effort consuming process, error prone and different
knowledge engineers will arrive at different models for the same sources of
law. Moreover, these models should be closely linked to the original sources
(and at the right level of detail, i.e. isomorphic) since these sources tend
to change over time and maintenance of the models is a serious problem.
This calls for tools and a method for supporting this modelling process and
increasing inter-coder reliability.
We have been researching a method to create isomorphic models semi-
automatically, focusing on (Dutch) laws. This article presents a next step in
this creation process.
1.1. General Approach
In order to achieve (semi-)automatic modelling of sources of law, we fol-
low a number of steps, as shown in Fig. 1. The process starts with the source
document, written in natural language (Dutch). Currently, we focus on laws,
though we hope to expand to other types of sources of law later on. We f‌irst
make the structure of the document explicit, by marking up the different
parts, such as chapters, paragraphs and sentences, and assigning identif‌iers
to each part. We then proceed to mark all references within the source to
E. de Maat is Researcher at the “Leibniz Center forLaw” (LCL); R. Winkels is Associate
Professor in Computer Science and Law at LCL of the Faculty of Law of the University of
186 Informatica e diritto /Proceedings of the Workshop LOAIT 2010
other sources of law, using a parser based on patterns for references1. This
structure and reference information is stored in CEN/MetaLex XML2.
Fig. 1 – Steps in automatic modelling of legal texts
The next step is to create models for each individual statement in the
text. In most cases, each sentence in Dutch law forms a complete statement
(though possibly part of a bigger construct), so we are, in fact, creating a
model for each sentence in the text. In the last step, these individual models
are integrated with each other to come to a complete model. In order to cre-
ate the models, we start by classifying each sentence in the text as a specif‌ic
provision, such as a def‌inition, a duty, or a modif‌ication of an earlier law. In
total, we recognise ten different main categories. As with the references, this
is done by automatic recognition of certain patterns in the text3.
For several types of sentences, these patterns, together with some added
features, are suff‌icient to extract all information needed to create a model of
the sentence. This is usually the case with sentences that are about the law
itself, instead of the subject matter of the law. These sentences are discussed
in Section 2. Other sentences, such as obligations, do focus on the subject
matter, and can vary wildly. Simple patterns will not suff‌ice to deal with
these sentences, and to extract information from these types of sentences, we
1E. DE MAAT, R. WI NKEL S, T. VAN ENGER S,Automated Detection of Reference Struc-
tures in Law, in Engers T.M.van (ed.), “Legal Knowledge and Information Systems, Proceed-
ings of the Jurix 2006 Nineteenth Annual Conference”, Amsterdam, IOS Press, 2006, pp.
3E. DE MAAT, R. WI NKEL S,Automatic Classif‌ication of Sentences in Dutch Laws, in
Francesconi E., Sartor G., Tiscornia D. (eds.), “Legal Knowledge and Information Systems,
Proceedings of the Jurix 2008 Twenty-First Annual Conference”, Amsterdam, IOS Press,
2008, pp. 207-216.

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