Search (45 results, page 1 of 3)

  • × theme_ss:"Automatisches Indexieren"
  1. Thiel, T.J.: Automated indexing of information stored on optical disk electronic document image management systems (1994) 0.07 
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    Source
    Encyclopedia of library and information science. Vol.54, [=Suppl.17]
  2. Zeng, L.: Automatic indexing for Chinese text : problems and progress (1992) 0.07 
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    Encyclopedia of library and information science. Vol.49, [=Suppl.12]
  3. Silvester, J.P.: Computer supported indexing : a history and evaluation of NASA's MAI system (1998) 0.07 
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    Encyclopedia of library and information science. Vol.61, [=Suppl.24]
  4. Salton, G.; Buckley, C.; Allan, J.: Automatic structuring of text files (1992) 0.04 
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    Abstract
    In many practical information retrieval situations, it is necessary to process heterogeneous text databases that vary greatly in scope and coverage and deal with many different subjects. In such an environment it is important to provide flexible access to individual text pieces and to structure the collection so that related text elements are identified and properly linked. Describes methods for the automatic structuring of heterogeneous text collections and the construction of browsing tools and access procedures that facilitate collection use. Illustrates these emthods with searches using a large automated encyclopedia
  5. Salton, G.: SMART System: 1961-1976 (2009) 0.04 
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    Source
    Encyclopedia of library and information sciences. 3rd ed. Ed.: M.J. Bates
  6. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  7. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.03 
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    Date
    20.10.2000 12:22:23
  8. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.03 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  9. Martins, E.F.; Belém, F.M.; Almeida, J.M.; Gonçalves, M.A.: On cold start for associative tag recommendation (2016) 0.03 
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    Abstract
    Tag recommendation strategies that exploit term co-occurrence patterns with tags previously assigned to the target object have consistently produced state-of-the-art results. However, such techniques work only for objects with previously assigned tags. Here we focus on tag recommendation for objects with no tags, a variation of the well-known \textit{cold start} problem. We start by evaluating state-of-the-art co-occurrence based methods in cold start. Our results show that the effectiveness of these methods suffers in this situation. Moreover, we show that employing various automatic filtering strategies to generate an initial tag set that enables the use of co-occurrence patterns produces only marginal improvements. We then propose a new approach that exploits both positive and negative user feedback to iteratively select input tags along with a genetic programming strategy to learn the recommendation function. Our experimental results indicate that extending the methods to include user relevance feedback leads to gains in precision of up to 58% over the best baseline in cold start scenarios and gains of up to 43% over the best baseline in objects that contain some initial tags (i.e., no cold start). We also show that our best relevance-feedback-driven strategy performs well even in scenarios that lack user cooperation (i.e., users may refuse to provide feedback) and user reliability (i.e., users may provide the wrong feedback).
  10. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
  11. Hauer, M.: Automatische Indexierung (2000) 0.02 
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt
  12. Fuhr, N.: Rankingexperimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:56
  13. Hauer, M.: Tiefenindexierung im Bibliothekskatalog : 17 Jahre intelligentCAPTURE (2019) 0.02 
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    Source
    B.I.T.online. 22(2019) H.2, S.163-166
  14. Biebricher, N.; Fuhr, N.; Lustig, G.; Schwantner, M.; Knorz, G.: ¬The automatic indexing system AIR/PHYS : from research to application (1988) 0.02 
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    Date
    16. 8.1998 12:51:22
  15. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.02 
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    Information und Märkte: 50. Deutscher Dokumentartag 1998, Kongreß der Deutschen Gesellschaft für Dokumentation e.V. (DGD), Rheinische Friedrich-Wilhelms-Universität Bonn, 22.-24. September 1998. Hrsg. von Marlies Ockenfeld u. Gerhard J. Mantwill
  16. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.02 
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    Date
    1. 4.2002 10:22:41
  17. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  18. Vinyals, O.; Toshev, A.; Bengio, S.; Erhan, D.: ¬A picture is worth a thousand (coherent) words : building a natural description of images (2014) 0.02 
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    Content
    "People can summarize a complex scene in a few words without thinking twice. It's much more difficult for computers. But we've just gotten a bit closer -- we've developed a machine-learning system that can automatically produce captions (like the three above) to accurately describe images the first time it sees them. This kind of system could eventually help visually impaired people understand pictures, provide alternate text for images in parts of the world where mobile connections are slow, and make it easier for everyone to search on Google for images. Recent research has greatly improved object detection, classification, and labeling. But accurately describing a complex scene requires a deeper representation of what's going on in the scene, capturing how the various objects relate to one another and translating it all into natural-sounding language. Many efforts to construct computer-generated natural descriptions of images propose combining current state-of-the-art techniques in both computer vision and natural language processing to form a complete image description approach. But what if we instead merged recent computer vision and language models into a single jointly trained system, taking an image and directly producing a human readable sequence of words to describe it? This idea comes from recent advances in machine translation between languages, where a Recurrent Neural Network (RNN) transforms, say, a French sentence into a vector representation, and a second RNN uses that vector representation to generate a target sentence in German. Now, what if we replaced that first RNN and its input words with a deep Convolutional Neural Network (CNN) trained to classify objects in images? Normally, the CNN's last layer is used in a final Softmax among known classes of objects, assigning a probability that each object might be in the image. But if we remove that final layer, we can instead feed the CNN's rich encoding of the image into a RNN designed to produce phrases. We can then train the whole system directly on images and their captions, so it maximizes the likelihood that descriptions it produces best match the training descriptions for each image.
  19. Banerjee, K.; Johnson, M.: Improving access to archival collections with automated entity extraction (2015) 0.02 
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    Abstract
    The complexity and diversity of archival resources make constructing rich metadata records time consuming and expensive, which in turn limits access to these valuable materials. However, significant automation of the metadata creation process would dramatically reduce the cost of providing access points, improve access to individual resources, and establish connections between resources that would otherwise remain unknown. Using a case study at Oregon Health & Science University as a lens to examine the conceptual and technical challenges associated with automated extraction of access points, we discuss using publically accessible API's to extract entities (i.e. people, places, concepts, etc.) from digital and digitized objects. We describe why Linked Open Data is not well suited for a use case such as ours. We conclude with recommendations about how this method can be used in archives as well as for other library applications.
  20. Lepsky, K.: Automatische Indexierung des Reallexikons zur Deutschen Kunstgeschichte (2006) 0.02 
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    Abstract
    Seriöse Information darf erwarten, wer renommierte Nachschlagewerke befragt. Zumindest für die über die Primärordnung (Stichwort/Lemma) erschlossenen Sachverhalte ist für Buchausgaben ein gezielter Zugriff möglich, Verweisungen zwischen Artikeln sorgen für weitere Einstiege. Anzunehmen ist, dass sich der Nutzen von Nachschlagewerken in elektronischer Form noch deutlich erhöhen lässt: Produkte wie z.B. "Brockhaus multimedial" oder "Encyclopedia Britannica" sorgen mit leistungsfähigen Techniken über den wahlfreien Volltextzugriff hinaus für zahlreiche Navigations- und Recherchemöglichkeiten. Es liegt daher nahe, über eine Digitalisierung konventionell vorliegender Nachschlagewerke auch deren Anwendung zu verbessern, die im Print möglichen Zugriffsmöglichkeiten deutlich zu erweitern. Beispiele für diesen Ansatz sind die Digitalisierung der "Oekonomischen Encyklopädie" von Johann Georg Krünitz, die mit hohem Aufwand nicht maschinell (Scanning und OCR) sondern manuell realisiert wurde oder auch die im "Projekt Runeberg' , vorgenommenen zahlreichen Digitalisierungen u.a. auch von Nachschlagewerken. Ob die einfache Volltextindexierung derartig umfangreicher und - weil bereits als Nachschlagewerk konzipiert - gleichzeitig extrem verdichteter Quellen für einen größtmöglichen Nutzen der elektronischen Version ausreicht, darf zu Recht bezweifelt werden. In kommerziellen Produkten sorgen daher zusätzliche Techniken für einerseits thematisch gezielte Zugriffe auch über Nicht-Stichwörter, andererseits für Querverbindungen zu möglicherweise weiteren Artikeln von Interesse ("Wissensnetz" des Brockhaus, "Knowledge Navigator" der Britannica). Es darf angenommen werden, dass derartige Techniken dabei auf Informationen aufsetzen können (Strukturierung der Artikel, gekennzeichnete (getaggte) Personennamen, Querverweise etc.), die in den zu verarbeitenden Artikeln in nutzbarer Form vorliegen. Für digitalisierte Druckausgaben kommen derartige Verfahren nicht in Frage, weil lediglich flache, noch dazu in der Regel mit OCR-Fehlern behaftete Texte vorliegen. Die Zugriffsmöglichkeiten bewegen sich daher zwischen der 1:1-Umsetzung der Druckausgabe, d.h. dem Primärzugriff über Stichwort, und der Volltextsuche auf den vollständigen Lexikontext. Beides ist angesichts der im elektronischen Volltext liegenden Möglichkeiten sicher nicht die Methode der Wahl. Für die Digitalisierung des "Reallexikons zur Deutschen Kunstgeschichte" im Rahmen des von der Deutschen Forschungsgemeinschaft geförderten Projekts "RDKWeb" wird daher versucht, mit den Mitteln der Automatischen Indexierung eine Lösung zu erzielen, die über eine reine Volltextsuchmöglichkeit hinaus Suchunterstützungen bietet, die sich an den Fähigkeiten kommerzieller Produkte orientieren (nicht messen!).

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