Search (22 results, page 1 of 2)

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Content

Vgl.: https://www.b-i-t-online.de/heft/2019-02-index.php.

Source

B.I.T.online. 22(2019) H.2, S.163-166

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Content

Vgl.: https://www.b-i-t-online.de/heft/2019-06-index.php.

Source

B.I.T.online. 22(2019) H.6, S.465-469

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Content

Vgl.: https://www.b-i-t-online.de/heft/2018-01-interview-moedden.pdf.

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Date

1. 2.2016 18:25:22

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Date

11. 9.2012 19:43:22

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Abstract

Große Aufmerksamkeit richtet sich im Moment auf das Potential von automatisierten Methoden in der Sacherschließung und deren Interaktionsmöglichkeiten mit intellektuellen Methoden. In diesem Kontext befasst sich der vorliegende Beitrag mit den folgenden Fragen: Was sind die Anforderungen an bibliothekarische Metadaten aus Sicht der Wissenschaft? Was wird gebraucht, um den Informationsbedarf der Fachcommunities zu bedienen? Und was bedeutet das entsprechend für die Automatisierung der Metadatenerstellung und -pflege? Dieser Beitrag fasst die von der Autorin eingenommene Position in einem Impulsvortrag und der Podiumsdiskussion beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

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Abstract

Spätestens seit dem Deutschen Bibliothekartag 2018 hat sich die Diskussion zu den automatischen Verfahren der Inhaltserschließung der Deutschen Nationalbibliothek von einer politisch geführten Diskussion in eine Qualitätsdiskussion verwandelt. Der folgende Beitrag beschäftigt sich mit Fragen der Qualität von Inhaltserschließung in digitalen Zeiten, wo heterogene Erzeugnisse unterschiedlicher Verfahren aufeinandertreffen und versucht, wichtige Anforderungen an Qualität zu definieren. Dieser Tagungsbeitrag fasst die vom Autor als Impulse vorgetragenen Ideen beim Workshop der FAG "Erschließung und Informationsvermittlung" des GBV am 29. August 2018 in Kiel zusammen. Der Workshop fand im Rahmen der 22. Verbundkonferenz des GBV statt.

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Imprint

Berlin : Humboldt-Universität zu Berlin, Philosophische Fakultät I, Institut für Bibliotheks- und Informationswissenschaft

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Abstract

The quality of stemming algorithms is typically measured in two different ways: (i) how accurately they map the variant forms of a word to the same stem; or (ii) how much improvement they bring to Information Retrieval systems. In this article, we evaluate various stemming algorithms, in four languages, in terms of accuracy and in terms of their aid to Information Retrieval. The aim is to assess whether the most accurate stemmers are also the ones that bring the biggest gain in Information Retrieval. Experiments in English, French, Portuguese, and Spanish show that this is not always the case, as stemmers with higher error rates yield better retrieval quality. As a byproduct, we also identified the most accurate stemmers and the best for Information Retrieval purposes.

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Content

"Until now, so-called computer vision has largely been limited to recognizing individual objects. The new software, described on Monday by researchers at Google and at Stanford University, teaches itself to identify entire scenes: a group of young men playing Frisbee, for example, or a herd of elephants marching on a grassy plain. The software then writes a caption in English describing the picture. Compared with human observations, the researchers found, the computer-written descriptions are surprisingly accurate. The advances may make it possible to better catalog and search for the billions of images and hours of video available online, which are often poorly described and archived. At the moment, search engines like Google rely largely on written language accompanying an image or video to ascertain what it contains. "I consider the pixel data in images and video to be the dark matter of the Internet," said Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory, who led the research with Andrej Karpathy, a graduate student. "We are now starting to illuminate it." Dr. Li and Mr. Karpathy published their research as a Stanford University technical report. The Google team published their paper on arXiv.org, an open source site hosted by Cornell University.
In living organisms, webs of neurons in the brain vastly outperform even the best computer-based networks in perception and pattern recognition. But by adopting some of the same architecture, computers are catching up, learning to identify patterns in speech and imagery with increasing accuracy. The advances are apparent to consumers who use Apple's Siri personal assistant, for example, or Google's image search. Both groups of researchers employed similar approaches, weaving together two types of neural networks, one focused on recognizing images and the other on human language. In both cases the researchers trained the software with relatively small sets of digital images that had been annotated with descriptive sentences by humans. After the software programs "learned" to see patterns in the pictures and description, the researchers turned them on previously unseen images. The programs were able to identify objects and actions with roughly double the accuracy of earlier efforts, although still nowhere near human perception capabilities. "I was amazed that even with the small amount of training data that we were able to do so well," said Oriol Vinyals, a Google computer scientist who wrote the paper with Alexander Toshev, Samy Bengio and Dumitru Erhan, members of the Google Brain project. "The field is just starting, and we will see a lot of increases."
Computer vision specialists said that despite the improvements, these software systems had made only limited progress toward the goal of digitally duplicating human vision and, even more elusive, understanding. "I don't know that I would say this is 'understanding' in the sense we want," said John R. Smith, a senior manager at I.B.M.'s T.J. Watson Research Center in Yorktown Heights, N.Y. "I think even the ability to generate language here is very limited." But the Google and Stanford teams said that they expect to see significant increases in accuracy as they improve their software and train these programs with larger sets of annotated images. A research group led by Tamara L. Berg, a computer scientist at the University of North Carolina at Chapel Hill, is training a neural network with one million images annotated by humans. "You're trying to tell the story behind the image," she said. "A natural scene will be very complex, and you want to pick out the most important objects in the image.""

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Abstract

Vor dem Hintergrund veränderter Ansprüche der Bibliotheksbenutzer an Recherchemöglichkeiten - weg vom klassischen Online-Katalog hin zum "One-Stop-Shop" mit Funktionalitäten wie thematisches Browsing, Relevanzranking und dergleichen mehr - einerseits und der notwendigen Bearbeitung von Massendaten (Stichwort Dokumentenflut) andererseits rücken Systeme zur automatischen Indexierung wieder verstärkt in den Mittelpunkt des Interesses. Da in Österreich die Beschäftigung mit diesem Thema im Bibliotheksbereich bislang nur sehr selektiv, bezogen auf wenige konkrete Projekte, erfolgte, wird zuerst ein allgemeiner theoretischer Überblick über die unterschiedlichen Verfahrensansätze der automatischen Indexierung geboten. Im nächsten Schritt werden mit der IDX-basierten Indexierungssoftware MILOS (mit den Teilprojekten MILOS I, MILOS II und KASCADE) und dem modularen System intelligentCAPTURE (mit der integrierten Indexierungssoftware AUTINDEX) die bis vor wenigen Jahren im deutschsprachigen Raum einzigen im Praxiseinsatz befindlichen automatischen Indexierungssysteme vorgestellt. Mit zunehmender Notwendigkeit, neue Wege der inhaltlichen Erschließung zu beschreiten, wurden in den vergangenen 5 - 6 Jahren zahlreiche Softwareentwicklungen auf ihre Einsatzmöglichkeit im Bibliotheksbereich hin getestet. Stellvertretend für diese in Entwicklung befindlichen Systeme zur automatischen inhaltlichen Erschließung wird das Projekt PETRUS, welches in den Jahren 2009 - 2011 an der DNB durchgeführt wurde und die Komponenten PICA Match&Merge sowie die Extraction Platform der Firma Averbis beinhaltet, vorgestellt.

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Abstract

Descriptive metadata play a key role in finding relevant search results in large amounts of unstructured data. However, current scientific audiovisual media are provided with little metadata, which makes them hard to find, let alone individual sequences. In this paper, the TIB / AV-Portal is presented as a use case where methods concerning the automatic generation of metadata, a semantic search and cross-lingual retrieval (German/English) have already been applied. These methods result in a better discoverability of the scientific audiovisual media hosted in the portal. Text, speech, and image content of the video are automatically indexed by specialised GND (Gemeinsame Normdatei) subject headings. A semantic search is established based on properties of the GND ontology. The cross-lingual retrieval uses English 'translations' that were derived by an ontology mapping (DBpedia i. a.). Further ways of increasing the discoverability and reuse of the metadata are publishing them as Linked Open Data and interlinking them with other data sets.

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Source

Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al

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Date

19. 8.2017 9:24:22

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Source

Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik