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Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, scalable indexing and search strategies (software) to meet a variety of user needs. Knowledge extraction or creation from text requires systematic yet reliable processing that can be codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

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Content

1. Einleitung 2. Ansatz und grundlegende Begriffe 3. Empirische Untersuchungen über das Suchverhalten in Online-Recherchen 4. Die Formulierung von Suchfragen in Online-Recherchen 5. Der Einfluß von Suchproblemen und Problembeschreibungen auf die Formulierung von Suchfragen 6. Die Empirische Untersuchung 7. Spezifizierung der Untersuchungsfragen 8. Allgemeines Vorgehen bei der Auswertung der Daten 9. Der Befehlsumfang der Recherchen und Suchfragen 10. Die thematische Organisation der Suchfragen 11. Die lexikalische Organisation der Suchfragen 12. Zusammenhänge zwischen den Suchfragen und den schriftlichen Problembeschreibungen 13. Die Transformation der Problemwörter in Suchausdrücke