NDLI: Knowledge acquisition in concept and document spaces by using self-organizing neural networks.

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Knowledge acquisition in concept and document spaces by using self-organizing neural networks.

Content Provider	CiteSeerX
Author	Winiwarter, Werner Schweighofer, Erich Merkl, Dieter
Abstract	. Exploratory data analysis seems to be a good tool for the acquisition and representation of the inherent knowledge in legal texts. The main difficulty besides the necessary input is the analysis of the various text and document structures. In our prototype CONCAT we use neural network technology to learn about the relations within the concept and document space of an existing domain. The results are quite encouraging because with existing input data a usable representation of the knowledge space can be obtained. 1 Introduction Exploratory data analysis seems to be a good tool for the representation of the inherent knowledge in legal texts. Existing legal information retrieval systems do not satisfy the demands of lawyers because they provide only a syntactic representation of the legal data (e.g. statutes, treaties, court decisions or literature). Advanced formalisations of legal knowledge exist in the form of legal expert systems or conceptual information retrieval systems. The ma...
File Format	PDF
Access Restriction	Open
Subject Keyword	Document Space Knowledge Acquisition Self-organizing Neural Network Legal Text Inherent Knowledge Good Tool Main Difficulty Legal Data Knowledge Space Legal Knowledge Exist Usable Representation Exploratory Data Analysis Advanced Formalisation Syntactic Representation Prototype Concat Various Text Document Structure Court Decision Introduction Exploratory Data Analysis Neural Network Technology Legal Expert System Conceptual Information Retrieval System Input Data Existing Legal Information Retrieval System Necessary Input
Content Type	Text

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