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This paper presents a study on the comprehensibility of rephrased syntactic structures in German court decisions. While there are a number of studies using psycholinguistic methods to investigate the comprehensibility of original legal texts, we are not aware of any study looking into the effect resolving complex structures has on the comprehensibility. Our study combines three methodological steps. First, we analyse an annotated corpus of court decisions, press releases and newspaper reports on these decisions in order to detect those complex structures in the decisions which distinguish them from the other text types. Secondly, these structures are rephrased into two increasingly simple versions. Finally, all versions are subjected to a self paced reading experiment. The findings suggest that rephrasing greatly enhances the comprehensibility for the lay reader.
Contemporary studies on the characteristics of natural language benefit enormously from the increasing amount of linguistic corpora. Aside from text and speech corpora, corpora of computer-mediated communication (CMC) Position themselves between orality and literacy, and beyond that provide in- sight into the impact of "new", mainly intemet-based media on language beha- viour. In this paper, we present an empirical attempt to work with annotated CMC corpora for the explanation of linguistic phenomena. In concrete terms, we implement machine leaming algorithms to produce decision trees that reveal rules and tendencies about the use of genitive markers in German.
Contemporary studies on the characteristics of natural language benefit enormously from the increasing amount of linguistic corpora. Aside from text and speech corpora, corpora of computer-mediated communication (CMC) position themselves between orality and literacy, and beyond that provide insight into the impact of “new”, mainly internet-based media on language behaviour. In this paper, we present an empirical attempt to work with annotated CMC corpora for the explanation of linguistic phenomena. In concrete terms, we implement machine learning algorithms to produce decision trees that reveal rules and tendencies about the use of genitive markers in German.