De Meyer, Geert

Opsomer, Rob

Van Eetvelde, Greet

English

Ghent University Academic Bibliography

Why it is necessary for legislators toannotate legislation with meta-dataRob OPSOMER a, Geert DE MEYER b, and Greet VAN EETVELDE aa Ghent University, Dep. of Civil Techniques, Environmental and Spatial Managementb Flemish Institute for Technological ResearchAbstract. Everyone is supposed to know the law. Since it is not possible for all per-sons to know all law, persons should be enabled to retrieve all legislation applicableto a certain issue. We argue that this should be done by tagging laws with labelsindicating their content. Although such techniques were already introduced yearsago, they are still only deployed on a very small scale. The main problem with ex-isting approaches is that the laws are not tagged by the legislators themselves, butby third party legislation managers. This third party tagging is either done manuallyby legal experts, or automated with the help of artificial intelligence techniques. Weargue that both of these approaches are inherently flawed, as automated approachesdo not work well, and manual tagging is prohibitively slow, expensive, and error-prone. Therefore, it is necessary that legislation is tagged at creation time, by thesame legislators who create it.Keywords. legislation, legislator, information retrieval, legal text retrieval1. IntroductionNemo censetur ignorare legem and Ignorantia juris non excusat are two of the bestknown legal adages. Both have essentially the same meaning: everyone is supposed toknow the law. It is a fundamental property of all modern legal systems [1]. It is a nec-essary property: accepting ignorance of law as a defense would make it impossible forcourts to function properly [2], as every defendant could plea he did not know the law hewas breaching. Since it is such a fundamental property, governments should enable theircitizens to fulfill it.There exists too much legislation, however, to expect every person to know everyarea of legislation by heart. According to Matthijs, there are about 45000 active lawsin Belgium [3]. Even worse, the publishing rate of legislative documents, visualized forBelgium in Figure 11, is both very high and ever-increasing, making it practically im-possible to stay up to date with all new legislation. Now should it be concluded that oneof the most fundamental adages in modern legal systems is unattainable? Not exactly.While it is impossible to be well informed about all legislation, this is not a strict ne-cessity in practice. Indeed, more important is that every person knows the legislationrelevant to him. Of course, to be able to know all legislation relevant to a certain issue,1Source: Belgian Federal Government, http://www.ejustice.just.fgov.be/wet/wet.htmpersons should first of all be able to find all legislation relevant to that issue. Reading alllaws is obviously not an option. Therefore, tools that enable persons to easily find legis-lation about a certain topic are required. Thus, the government duty of enabling citizensto know the law boils down to creating good information retrieval tools.01000200030004000500060007000194619491952195519581961196419671970197319761979198219851988199119941997200020032006# New legislative publicationsTime periodFigure 1. Number of new legislative publications in Belgium, 1946-2007The remainder of this paper is structured as follows. In section 2, the field of infor-mation retrieval (IR) is explained, with a focus on the retrieval of legislation. In section 3,one specific IR technique, classification schemes, in which persons can choose a certainclass of legislation and get an overview of relevant laws and articles for that class, is dis-cussed. In section 4, we discuss different ways of creating and maintaining the mappingbetween laws and class labels, and conclude that legislators should add class labels to thelegislation they create. A short summary, conclusions and directions for future researchare written in section 5.2. Information RetrievalInformation Retrieval (IR) is a subfield of computer science, concerned with finding in-formation in a a large data collection. More formally, it is "the process that selects docu-ments relevant to a user’s information needs out of a (large) document collection" [4, 5].A typical basic IR system allows the user to enter a search query, composed of keywordsand modifiers such as AND and OR [4]. The system then searches documents that con-tain the desired words. Over time, the capabilities of this kind of IR systems have im-proved greatly. Nowadays, such systems can handle user queries in a more reliable way,and are often able to recognize and deal with issues as synonyms, word inflections, andtyping errors [4]. Search results are often ranked according to some relevance measure,and sometimes even automatically clustered in meaningful result groups [5].Despite these improvements, this kind of IR systems suffers from a fundamentalproblem: they expect the user to come up with good keywords. While this is not abig problem when a user searches for a easily defined general topics as "football" or"Barcelona", it is for complex legislative concepts such as "Enforcement", since thesecomplex legal concepts do not correspond with explicit wording in document texts [6].Also, while in general IR tasks the goal is to find some or most relevant information,in a legal setting, one should be able to find all, or almost all legislation relevant to acertain issue [6]. If a legislative IR tool failed to retrieve only one crucial article for acertain query, the tool would actually do more harm than good for that query. Therefore,it becomes clear that keyword search is not very appropriate in the context of the re-trieval of legislation. Thus, more advanced IR schemes are necessary for the retrieval oflegislation [6, 7]. In our opinion, the only viable solution to these problems is the use ofa classification scheme, in which laws and articles are classified into legal topics. Thatway, users can browse through these predefined classes of law, and can thus easily findall legislation related to their specific issue. This kind of classification is discussed inmore detail in the following section.3. Classification schemes to access legislationThe use of a classification scheme boils down to tagging articles with class (index) labels,and consequently offering users the possibility of selecting labels and getting a list ofrelated articles as a result.Good classification schemes have three structural properties. First of all, indexescan be organized into a hierarchic structure. For example, the main index "Enforcement"could be subdivided into "Criminal enforcement", "Civil enforcement", and "Adminis-trative enforcement". Second, the classification can happen along more than one axis.For example, there could be a thematic axis, dividing law into topics such as "Tax Leg-islation", "Environmental Legislation", and "Energy Legislation", and a legal axis, withtopics such as "Enforcement" or "Procedures". Every article then is assigned a label foreach axis. A third, and final property is that articles can also have different index labelswithin one axis. For example, an article could contain information about both "Civil en-forcement" and "Administrative enforcement", and should therefore receive both labels.For users, such a classification scheme offers great usability and access. Even with-out knowing the specific terminology of a certain legal domain, one can easily browsethrough the different levels of the classification hierarchies to find the legislation of hisor her interest.This plea for a classification approach to legislative information retrieval is not new(e.g. [6, 8]). Nevertheless, despite the advantages, this classification approach is still notwidely adopted. Most of the currently existing initiatives offering online access to legis-lation still only offer simple keyword search, often even without the improvements dis-cussed above, aggravating the problems of this kind of search. This slow adaptation ofclassification schemes is due to one big disadvantage of such scheme, namely that thecreator of the classification scheme has to classify articles into the classification scheme.In existing systems, this classification task is always the responsibility of the IR systembuilders and content managers. This approach is, however, as we will argue hereafter, aflawed one.Usually, these content managers employ legal experts to classify the articles man-ually [6]. This is prohibitively slow and expensive, especially because of the high rateat which new legislation is created. On top of that, even legal experts make mistakes.An obvious alternative is to automate this task. This approach, while cheap and fast, hasproblems of its own. While text classification is a mature domain, with very good re-sults in e.g. the automated classification of news articles in classes such as "Politics" or"Sport" [9], achieving accuracy rates of 90%+, the approach does not work very well forlegal topics [6, 10]. This is mainly because legal concepts are too abstract to be easilylearned by a classifier. In the past, there was always optimism that this problem wouldbe overcome, but now we have indications that this will not be the case in the near fea-ture [11].Thus, while the classification approach is necessary from a user perspective, it isvery hard to create such a classification if the content managers have to add the classlabels after the legislation is created. Therefore, the only feasible option is that legislatorsthemselves tag new legislation with appropriate labels, a path we pursue in the nextsection.4. Legislators should do the classificationAs there is no practical way to classify legislation after it is issued, legislation should betagged with class labels at the time it is created. The people most suited to do this wouldbe the legislators themselves. As they are obviously very familiar with the legislationthey create, the extra effort required to tag it with appropriate thematic and legal classlabels should be negligible.Of course, the tagging of new legislation does not solve the problem of legacy legis-lation. However, as shown in Figure 1, the amount of new legislation is ever-increasing,and thus poses the greatest challenge in the long term. Also, indexing all existing legis-lation is a huge task but, at least, a one-time and finite effort. This is strongly opposed tothe continuous, never-ending task of indexing all new legislation.5. ConclusionsIn this paper, we argue that legislators should tag every piece of legislation they producewith class labels. While the idea of adding class labels to legislation is not new, here wehave shown that doing it after legislation is issued is practically impossible, whether donemanually or automatically. Also, we have argued that adding class labels to legislation isnot only desirable, but absolute necessary to safeguard the ability of persons to fulfill theadage Nemo censetur ignorare legem.References[1] Laurence D. Houlgate. Ignorantia juris: A plea for justice. Ethics, 78(1):32–42, 1967.[2] John Austin. Lectures on Jurisprudence, pages 496–501. London, 1873.[3] H. Matthijs. To appear.[4] Ricardo Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 1stedition, May 1999.[5] Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. Introduction to Information Re-trieval. Cambridge University Press, 1 edition, July 2008.[6] Marie-Francine Moens. Innovative techniques for legal text retrieval. Artif. Intell. Law, 9(1):29–57,2001.[7] J. P. Dick. Conceptual retrieval and case law. In ICAIL ’87: Proceedings of the 1st internationalconference on Artificial intelligence and law, pages 106–115, New York, NY, USA, 1987. ACM.[8] C. Biagioli. Towards a legal rules functional micro-ontology. In Proc. of workshop LEGONT ’97, 1997.[9] F. Sebastiani. Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1–47,2002.[10] R. Opsomer, G. De Meyer, C. Cornelis, and G. Van Eetvelde. Exploiting properties of legislative textsto improve classification accuracy. To be published in proceedings of Jurix 2009, 2009.[11] R. Opsomer, G. De Meyer, and G. Van Eetvelde. Automated classification of legal texts will never work.TO BE WRITTEN AND PUBLISHED.

Why it is necessary for legislators to annotate legislation with meta-data

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Why it is necessary for legislators to annotate legislation with meta-data

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