Emile de Maat

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Combining legal content stores of different providers is usually time, effort and money intensive due to the usually ’hard-wired’ links between different parts of the constituting sources within those stores. In practice users of legal content are confronted with a vendor lock-in situation and have to find work-arounds when they want to combine their own(More)
This paper presents results of an experiment in which we used machine learning (ML) techniques to classify sentences in Dutch legislation. These results are compared to the results of a pattern-based classifier. Overall, the ML classifier performs as accurate (>90%) as the pattern based one, but seems to generalize worse to new laws. Given these results,(More)
In a lot of existing legal knowledge based applications, the underlying legal model does not retain isomorphism with the original legal text. Parts of the text have not been modelled, and other parts have been simplified to single if-thenelse clauses. This makes these models difficult to validate, maintain and re-use. In this article we propose to make an(More)
The Dutch Tax and Customs Administration (DTCA) is one of many organizations that deal with a multitude of electronic legal data, from various sources and in different formats. In this paper, we describe the results of a study aimed at better access to these sources by having a supplier and format independent knowledge store that describes the sources and(More)
Currently almost all legislative bodies throughout Europe use general purpose word-processing software for the drafting of legal documents. These regular word processors do not provide specific support for legislative drafters and parliamentarians to facilitate the legislative process. Furthermore, they do not natively support metadata on regulations. This(More)