This paper is positioned within the development of an automated indexing system for the CISMeF quality controlled health gateway. For disambiguation purposes, we wish to perform text categorization prior to indexing. Hence, a global approach contrasting with the classical analytical methods based on the analysis of keyword counts extracted from the text is necessary. The use of statistical compression models enables us to proceed avoiding keyword extraction at this stage. Preliminary results show that although this method is not as precise as others in terms of resource categorization, it can significantly benefit indexing.
In this paper we also propose a method for the detection of selected medical terms in medical documents. The system is based on the training of an artificial neural network using documents containing the terms and documents not containing the terms. The trained neural network is then used to evaluate the presence of the terms in unknown documents. This approach is compared with the approach of using word counts and with a statistical method based on a combination of word counts and TF/IDF scores. It is shown that the proposed approach performs better than the traditional methods, and is especially effective for the detection of terms used by providers in medical forms.
A method for de-noising data is proposed. A digital signature based on a statistical compression model generates a compact representation of a text. This representation is then used to automatically categorize texts in order to obtain the main topics and to identify groups of documents that are similar to each other. This approach is compared with the traditional methods that rely on keyword extraction, word frequencies analysis, and information retrieval techniques. It is shown that although the proposed approach has lower precision than others, it is more effective as it allows us to dramatically reduce the number of contents to analyze while keeping good precision.
Some applications, such as Java, require that the application directories and libraries are updated to a newer version before the user can update. However, there are some applications that are not explicitly API-specific, such as the X Window System, the X server source code, are automatically updated as required.
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