The number of legal documents is continuously increasing but technology insufficiently supports knowledge-intensive processes on these documents. This work shows the potentials of data and text mining for semantic analysis, in particular natural language processing (NLP), of legal documents. The analysis uses powerful and adapted software architectures, e.g. Apache UIMA and Apache Spark, to facilitate interpretation of statutory texts and the creation of executable models in meta-model-based information systems (IS).
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The number of legal documents is continuously increasing but technology insufficiently supports knowledge-intensive processes on these documents. This work shows the potentials of data and text mining for semantic analysis, in particular natural language processing (NLP), of legal documents. The analysis uses powerful and adapted software architectures, e.g. Apache UIMA and Apache Spark, to facilitate interpretation of statutory texts and the creation of executable models in meta-model-based inf...
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