In this paper we presentPEA,aPersonal Email Assistant, which lters incoming emails and ranks them according to their relevance. We provide tools for the acquisition of individual user models, which may consist of several pro les to map various interest domains of the user. In order to respond promptly to the shifts of interests of a user, we apply evolutionary algorithms to support an adaptive environment that constantly adjusts the user model to improve the quality of relevance assessment. As second adaptive component wemake use of a monitoring module that records all activities of the user. By means of a classi er system we model the behavior of the user to predict future actions, which results rst in suggestions to the user and later in automatically performed tasks. Additional features of the system include the segmentation of lengthy emails, e cient treatment of duplicate or new versions of messages, cross-language ltering, and the extraction of relevant information by using templates learned from examples. 1

. Exploratory analysis is an area of increasing interest in the computational linguistics arena. Pragmatically speaking, exploratory analysis may be paraphrased as natural language processing by means of analyzing large corpora of text. Concerning the analysis, appropriate means are statistics, on the one hand, and artificial neural networks, on the other hand. As a challenging application area for exploratory analysis of text corpora we may certainly identify text databases, be it information retrieval or information filtering systems. With this paper we present recent findings of exploratory analysis based on both statistical and neural models applied to legal text corpora. Concerning the artificial neural networks, we rely on a model adhering to the unsupervised learning paradigm. This choice appears naturally when taking into account the specific properties of large text corpora where one is faced with the fact that input-output-mappings as required by supervised learning models ca...

. 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...

. This paper is concerned with the application of Kohonen's self-organising feature map to legal knowledge acquisition. More precisely, the map is used for analysis of legal thesauri which are obtained by means of connotation analysis of the individual document descriptors. The outcome of the learning process of the artificial neural network is further used to distinguish between precise legal terms and terms with rather fuzzy meaning. INTRODUCTION The formalisation of legal data, e.g. statutes, court decisions or treaties, constitutes a necessary prerequisite for advanced legal information processing. Common approaches to cope with this problem are first, rewriting the law as a logic program and second, performing linguistic analysis of the legal language. The former approach represents a harsh simplification of the complexity of legal systems and thus leads to severe limitations as for example the open texture problem. Inherent to the latter approach is the obvious fact that so far...

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 processing. This selectivity is obtained by means of connotation analysis of the individual descriptors which makes it possible to detect hidden word meanings and to distinguish between precise legal terms and words with fuzzy meaning. Within the prototype system CONCAT we applied both a statistical and a connectionist approach to connotation analysis and performed a comparative evaluation of the achieved results. 1 Introduction Advanced use of information technology in the legal field requires formalisation of the legal data (e.g. statutes, treaties, court decisions or literature). Two main approaches are concerned with this ...