Erich Schweighofer

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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)
Legal Information Retrieval (IR) research has stressed the fact that legal knowledge systems should be sufficiently capable to interpret and handle the semantics of a database. Modeling (expert-) knowledge by using ontologies enhances the ability to extract and exploit information from documents. This contribution presents theories, ideas and notions(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)
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 data for the connectionist part of the system. Next, we will provide a description of the artificial neural network model, namely the self-organising(More)
Knowledge acquisition constitutes the bottleneck for the creation of legal expert systems. A certain degree of formalism of legal language is an inevitable prerequisite. Our prototype KONTERM deals with that problem by supporting the process of creating a selective thesaurus for a legal information system which can be used for automatic indexing and(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)