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The huge text archives and retrieval systems of legal information have not achieved yet the representation in the well-known subject-oriented structure of legal commentaries. Content-based classification and text analysis remains a high priority research topic. In the joint KONTERM, SOM and LabelSOM projects, learning techniques of neural networks are used(More)
The classification of feature vectors representing the interpretation of legal documents improves the search for similar or related documents, the interpretation of these documents as well as the navigation within the text corpus. The need for effective approaches of classification is dramatically increased nowadays due to the advent of massive digital(More)
However, manual indexing that would improve the exactness of the descriptors represents no practicable alternative in case of large document collections. The remainder of this paper is organised as follows. In the following section we will briefly describe the test environment of the system. Particularly, we will summarize the approach to acquire the input(More)
We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the(More)
Knowledge acquisition constitutes the bottleneck for the creation of legal expert systems. Legal language must be formalised to such a degree that it can be processed automatically. We deal with this problem by supporting the process of creating a selective thesaurus for a legal information system which can be seen as prerequisite for further knowledge(More)
Our legal expert system KONTERM contains a selective thesaurus and a knowledge base for the automatic representation of the structure and the contents of the document. The thesaurus takes into account the necessary degree of formalism of legal language and therefore overcomes the untidiness of natural language and represents automatically the expert(More)
Traditional information retrieval systems do not satisfy the lawyers' demands because they provide only syntactic representation of legal data. The bottleneck for the creation of the more promising conceptual information retrieval systems is the time-consuming knowledge acquisition. The best solution is the representation of legal knowledge by simple(More)
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