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Footnote

Rez. in: Mitt VÖB 64(2911) H.3/4, S.547-553 (O. Oberhauser): "Dieser mit 156 Seiten (inklusive Register) relativ schmale Band enthält sieben mit dem Gütesiegel "peer-reviewed" versehene Beiträge namhafter Autoren zu "research fronts" auf dem Gebiet des Information Retrieval (IR) - ein Begriff, der hier durchaus breit verstanden wird. Wie die Herausgeber Allen Foster und Pauline Rafferty - beide aus dem Department of Information Studies an der Aberystwyth University (Wales) - in ihrer Einleitung betonen, sind Theorie und Praxis der Wissensorganisation im Internet- Zeitalter nicht mehr nur die Domäne von Informationswissenschaftlern und Bibliotheksfachleuten, sondern auch von Informatikern, Semantic-Web-Entwicklern und Wissensmanagern aus den verschiedensten Institutionen; neben das wissenschaftliche Interesse am Objektbereich ist nun auch das kommerzielle getreten. Die Verarbeitung von Massendaten, die Beschäftigung mit komplexen Medien und die Erforschung der Möglichkeiten zur Einbeziehung der Rezipienten sind insbesondere die Aspekte, um die es heute geht. ..." Weitere Rez. in: Library review 61(2012) no.3, S.233-235 (G. Macgregor); J. Doc. 69(2013) no.2, S.320-321 (J. Bates)

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RSWK

Digitale Daten / Langzeitarchivierung / Aufsatzsammlung

Subject

Digitale Daten / Langzeitarchivierung / Aufsatzsammlung

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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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Classification

006.7 22

Date

7. 3.2007 19:30:22

DDC

006.7 22

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Date

22. 3.2008 18:05:16

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Date

23. 3.2008 19:10:22