Search (33 results, page 1 of 2)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.08 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002) 0.03 
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  3. Jersek, T.: Automatische DDC-Klassifizierung mit Lingo : Vorgehensweise und Ergebnisse (2012) 0.03 
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    Pages
    V. 56 S
  4. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.03 
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    Abstract
    This study seeks to find out how human beings cluster Web pages naturally. Twenty Web pages retrieved by the Northem Light search engine for each of 10 queries were sorted by 3 subjects into categories that were natural or meaningful to them. lt was found that different subjects clustered the same set of Web pages quite differently and created different categories. The average inter-subject similarity of the clusters created was a low 0.27. Subjects created an average of 5.4 clusters for each sorting. The categories constructed can be divided into 10 types. About 1/3 of the categories created were topical. Another 20% of the categories relate to the degree of relevance or usefulness. The rest of the categories were subject-independent categories such as format, purpose, authoritativeness and direction to other sources. The authors plan to develop automatic methods for categorizing Web pages using the common categories created by the subjects. lt is hoped that the techniques developed can be used by Web search engines to automatically organize Web pages retrieved into categories that are natural to users. 1. Introduction The World Wide Web is an increasingly important source of information for people globally because of its ease of access, the ease of publishing, its ability to transcend geographic and national boundaries, its flexibility and heterogeneity and its dynamic nature. However, Web users also find it increasingly difficult to locate relevant and useful information in this vast information storehouse. Web search engines, despite their scope and power, appear to be quite ineffective. They retrieve too many pages, and though they attempt to rank retrieved pages in order of probable relevance, often the relevant documents do not appear in the top-ranked 10 or 20 documents displayed. Several studies have found that users do not know how to use the advanced features of Web search engines, and do not know how to formulate and re-formulate queries. Users also typically exert minimal effort in performing, evaluating and refining their searches, and are unwilling to scan more than 10 or 20 items retrieved (Jansen, Spink, Bateman & Saracevic, 1998). This suggests that the conventional ranked-list display of search results does not satisfy user requirements, and that better ways of presenting and summarizing search results have to be developed. One promising approach is to group retrieved pages into clusters or categories to allow users to navigate immediately to the "promising" clusters where the most useful Web pages are likely to be located. This approach has been adopted by a number of search engines (notably Northem Light) and search agents.
    Date
    12. 9.2004 9:56:22
  5. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  6. Sommer, M.: Automatische Generierung von DDC-Notationen für Hochschulveröffentlichungen (2012) 0.02 
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    Pages
    V, 67 S
  7. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.02 
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    Date
    22. 8.2009 12:54:24
  8. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.02 
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    Date
    1. 2.2016 18:25:22
  9. Rooney, N.; Patterson, D.; Galushka, M.; Dobrynin, V.; Smirnova, E.: ¬An investigation into the stability of contextual document clustering (2008) 0.02 
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  10. Borodin, Y.; Polishchuk, V.; Mahmud, J.; Ramakrishnan, I.V.; Stent, A.: Live and learn from mistakes : a lightweight system for document classification (2013) 0.02 
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  11. Bianchini, C.; Bargioni, S.: Automated classification using linked open data : a case study on faceted classification and Wikidata (2021) 0.01 
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    Abstract
    The Wikidata gadget, CCLitBox, for the automated classification of literary authors and works by a faceted classification and using Linked Open Data (LOD) is presented. The tool reproduces the classification algorithm of class O Literature of the Colon Classification and uses data freely available in Wikidata to create Colon Classification class numbers. CCLitBox is totally free and enables any user to classify literary authors and their works; it is easily accessible to everybody; it uses LOD from Wikidata but missing data for classification can be freely added if necessary; it is readymade for any cooperative and networked project.
  12. Search Engines and Beyond : Developing efficient knowledge management systems, April 19-20 1999, Boston, Mass (1999) 0.01 
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    Content
    Ramana Rao (Inxight, Palo Alto, CA) 7 ± 2 Insights on achieving Effective Information Access Session One: Updates and a twelve month perspective Danny Sullivan (Search Engine Watch, US / England) Portalization and other search trends Carol Tenopir (University of Tennessee) Search realities faced by end users and professional searchers Session Two: Today's search engines and beyond Daniel Hoogterp (Retrieval Technologies, McLean, VA) Effective presentation and utilization of search techniques Rick Kenny (Fulcrum Technologies, Ontario, Canada) Beyond document clustering: The knowledge impact statement Gary Stock (Ingenius, Kalamazoo, MI) Automated change monitoring Gary Culliss (Direct Hit, Wellesley Hills, MA) User popularity ranked search engines Byron Dom (IBM, CA) Automatically finding the best pages on the World Wide Web (CLEVER) Peter Tomassi (LookSmart, San Francisco, CA) Adding human intellect to search technology Session Three: Panel discussion: Human v automated categorization and editing Ev Brenner (New York, NY)- Chairman James Callan (University of Massachusetts, MA) Marc Krellenstein (Northern Light Technology, Cambridge, MA) Dan Miller (Ask Jeeves, Berkeley, CA) Session Four: Updates and a twelve month perspective Steve Arnold (AIT, Harrods Creek, KY) Review: The leading edge in search and retrieval software Ellen Voorhees (NIST, Gaithersburg, MD) TREC update Session Five: Search engines now and beyond Intelligent Agents John Snyder (Muscat, Cambridge, England) Practical issues behind intelligent agents Text summarization Therese Firmin, (Dept of Defense, Ft George G. Meade, MD) The TIPSTER/SUMMAC evaluation of automatic text summarization systems Cross language searching Elizabeth Liddy (TextWise, Syracuse, NY) A conceptual interlingua approach to cross-language retrieval. Video search and retrieval Armon Amir (IBM, Almaden, CA) CueVideo: Modular system for automatic indexing and browsing of video/audio Speech recognition Michael Witbrock (Lycos, Waltham, MA) Retrieval of spoken documents Visualization James A. Wise (Integral Visuals, Richland, WA) Information visualization in the new millennium: Emerging science or passing fashion? Text mining David Evans (Claritech, Pittsburgh, PA) Text mining - towards decision support
  13. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22
  14. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  15. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
  16. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.01 
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    Date
    1. 8.1996 22:08:06
  17. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01 
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    Date
    22. 7.2006 16:24:52
  18. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01 
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    Date
    22. 9.2008 18:31:54
  19. Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005) 0.01 
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    Footnote
    Rez. in: VÖB-Mitteilungen 58(2005) H.3, S.102-104 (R.F. Müller); ZfBB 53(2006) H.5, S.282-283 (L. Svensson): "Das Sammeln und Verzeichnen elektronischer Ressourcen gehört in wissenschaftlichen Bibliotheken längst zum Alltag. Parallel dazu kündigt sich ein Paradigmenwechsel bei den Findmitteln an: Um einen effizienten und benutzerorientierten Zugang zu den gemischten Kollektionen bieten zu können, experimentieren einige bibliothekarische Diensteanbieter wie z. B. das hbz (http://suchen.hbz-nrw.de/dreilaender/), die Bibliothek der North Carolina State University (www.lib.ncsu.edu/) und demnächst vascoda (www.vascoda.de/) und der Librarians-Internet Index (www.lii.org/) zunehmend mit Suchmaschinentechnologie. Dabei wird angestrebt, nicht nur einen vollinvertierten Suchindex anzubieten, sondern auch das Browsing durch eine hierarchisch geordnete Klassifikation. Von den Daten in den deutschen Verbunddatenbanken ist jedoch nur ein kleiner Teil schon klassifikatorisch erschlossen. Fremddaten aus dem angloamerikanischen Bereich sind oft mit LCC und/oder DDC erschlossen, wobei die Library of Congress sich bei der DDCErschließung auf Titel, die hauptsächlich für die Public Libraries interessant sind, konzentriert. Die Deutsche Nationalbibliothek wird ab 2007 Printmedien und Hochschulschriften flächendeckend mit DDC erschließen. Es ist aber schon offensichtlich, dass v. a. im Bereich der elektronischen Publikationen die anfallenden Dokumentenmengen mit immer knapperen Personalressourcen nicht intellektuell erschlossen werden können, sondern dass neue Verfahren entwickelt werden müssen. Hier kommt Oberhausers Buch gerade richtig. Seit Anfang der 1990er Jahre sind mehrere Projekte zum Thema automatisches Klassifizieren durchgeführt worden. Wer sich in diese Thematik einarbeiten wollte oder sich für die Ergebnisse der größeren Projekte interessierte, konnte bislang auf keine Überblicksdarstellung zurückgreifen, sondern war auf eine Vielzahl von Einzeluntersuchungen sowie die Projektdokumentationen angewiesen. Oberhausers Darstellung, die auf einer Fülle von publizierter und grauer Literatur fußt, schließt diese Lücke. Das selbst gesetzte Ziel, einen guten Überblick über den momentanen Kenntnisstand und die Ergebnisse der einschlägigen Projekte verständlich zu vermitteln, erfüllt der Autor mit Bravour. Dabei ist anzumerken, dass er ein bibliothekarisches Grundwissen und mindestens grundlegende Kenntnisse über informationswissenschaftliche Grundbegriffe und Fragestellungen voraussetzt, wobei hier für den Einsteiger einige Hinweise auf einführende Darstellungen wünschenswert gewesen wären.
    Zum Inhalt Auf einen kurzen einleitenden Abschnitt folgt eine Einführung in die grundlegende Methodik des automatischen Klassifizierens. Oberhauser erklärt hier Begriffe wie Einfach- und Mehrfachklassifizierung, Klassen- und Dokumentzentrierung, und geht danach auf die hauptsächlichen Anwendungen der automatischen Klassifikation von Textdokumenten, maschinelle Lernverfahren und Techniken der Dimensionsreduktion bei der Indexierung ein. Zwei weitere Unterkapitel sind der Erstellung von Klassifikatoren und den Methoden für deren Auswertung gewidmet. Das Kapitel wird abgerundet von einer kurzen Auflistung einiger Softwareprodukte für automatisches Klassifizieren, die sowohl kommerzielle Software, als auch Projekte aus dem Open-Source-Bereich umfasst. Der Hauptteil des Buches ist den großen Projekten zur automatischen Erschließung von Webdokumenten gewidmet, die von OCLC (Scorpion) sowie an den Universitäten Lund (Nordic WAIS/WWW, DESIRE II), Wolverhampton (WWLib-TOS, WWLib-TNG, Old ACE, ACE) und Oldenburg (GERHARD, GERHARD II) durchgeführt worden sind. Der Autor beschreibt hier sehr detailliert - wobei der Detailliertheitsgrad unterschiedlich ist, je nachdem, was aus der Projektdokumentation geschlossen werden kann - die jeweilige Zielsetzung des Projektes, die verwendete Klassifikation, die methodische Vorgehensweise sowie die Evaluierungsmethoden und -ergebnisse. Sofern Querverweise zu anderen Projekten bestehen, werden auch diese besprochen. Der Verfasser geht hier sehr genau auf wichtige Aspekte wie Vokabularbildung, Textaufbereitung und Gewichtung ein, so dass der Leser eine gute Vorstellung von den Ansätzen und der möglichen Weiterentwicklung des Projektes bekommt. In einem weiteren Kapitel wird auf einige kleinere Projekte eingegangen, die dem für Bibliotheken besonders interessanten Thema des automatischen Klassifizierens von Büchern sowie den Bereichen Patentliteratur, Mediendokumentation und dem Einsatz bei Informationsdiensten gewidmet sind. Die Darstellung wird ergänzt von einem Literaturverzeichnis mit über 250 Titeln zu den konkreten Projekten sowie einem Abkürzungs- und einem Abbildungsverzeichnis. In der abschließenden Diskussion der beschriebenen Projekte wird einerseits auf die Bedeutung der einzelnen Projekte für den methodischen Fortschritt eingegangen, andererseits aber auch einiges an Kritik geäußert, v. a. bezüglich der mangelnden Auswertung der Projektergebnisse und des Fehlens an brauchbarer Dokumentation. So waren z. B. die Projektseiten des Projekts GERHARD (www.gerhard.de/) auf den Stand von 1998 eingefroren, zurzeit [11.07.06] sind sie überhaupt nicht mehr erreichbar. Mit einigem Erstaunen stellt Oberhauser auch fest, dass - abgesehen von der fast 15 Jahre alten Untersuchung von Larsen - »keine signifikanten Studien oder Anwendungen aus dem Bibliotheksbereich vorliegen« (S. 139). Wie der Autor aber selbst ergänzend ausführt, dürfte dies daran liegen, dass sich bibliografische Metadaten wegen des geringen Textumfangs sehr schlecht für automatische Klassifikation eignen, und dass - wie frühere Ergebnisse gezeigt haben - das übliche TF/IDF-Verfahren nicht für Katalogisate geeignet ist (ibd.).
  20. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.01 
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    Abstract
    Authors describe the background and the work involved in setting up Engine-e, a Web index that uses automatic classification as a mean for the selection of resources in Engineering. Considerations in offering a robot-generated Web index as a successor to a manually indexed quality-controlled subject gateway are also discussed

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