Abstract
Within the OpenLaws.eu project, we attempt to suggest relevant new sources of law to users of legal portals based on the documents they are focusing on at a certain moment in time, or those they have selected. In the future we attempt to do this both based on ‘objective’ features of the documents themselves and on ‘subjective’ information gathered from other users (‘crowdsourcing’). At this moment we concentrate on the first method. In Sect. 10.2 I describe how we create the web of law if it is not available in machine readable form, or extend it when that is necessary. Next, I present results of experiments using analysis of the network of references or citations to suggest these new documents. In Sect. 10.3 I describe two experiments where we mix the use of network analysis with similarity based on the comparison of the actual text of documents. One experiment is based on simple bag-of-words and normalisation, the other uses Latent Dirichlet Allocation (LDA) with added n-grams. A small formative evaluation in both experiments suggests that text similarity alone works better than network analysis alone or a combination, at least for Dutch court decisions.
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Notes
- 1.
These topics occur in a series of international workshops on “Network Analysis in Law (NAiL)” I have been organizing since 2013.
- 2.
- 3.
rechtspraak.nl.
- 4.
Recently one has started to add some links to cited legislation in metadata, but not all and it does not give information on where in the text the citation occurs, nor how often.
- 5.
In 2016 a new version of the Dutch legislative portal was launched that overcomes many of the problems.
- 6.
Precision gives the percentage of all references we find that is relevant or correct. Precision of 100% means all references we find are relevant or correct. Recall gives the percentage of all relevant or correct references there are, that we actually find. A recall of 100% would mean we find all relevant or correct references.
- 7.
Bibliographic conventions distinguish four levels of documents: work, expression, manifestation and item (Saur 1998).
- 8.
‘European Case Law Identifier’; see Council of the European Union conclusions on ECLI at: http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:52011XG0429(01).
- 9.
TfidfVectorizer of SciKit-learn (Pedregosa et al. 2011) was used with minimal document frequency set to 1 and maximum to.7.
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- 11.
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- 13.
Because we are interested in whether adding the bag-of-references to the similarity scores improves performance, documents that had recommendations that occurred in both sets were removed.
- 14.
MAchine Learning for LanguagE Toolkit, McCallum (2002).
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Acknowledgements
Part of this research is co-funded by the Civil Justice Programme of the European Union in the OpenLaws.eu project under grant JUST/2013/JCIV/AG/4562. I would like to thank my students: Erwin van den Berg, Bart Vredebregt and Wolf Vos who performed the experiments.
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Winkels, R. (2019). Exploiting the Web of Law. In: Boulet, R., Lajaunie, C., Mazzega, P. (eds) Law, Public Policies and Complex Systems: Networks in Action. Law, Governance and Technology Series, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-11506-7_10
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