Search (60 results, page 1 of 3)

  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2000 TO 2010}
  1. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.05 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
    Type
    a
  2. Hauer, M.: Automatische Indexierung (2000) 0.04 
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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
    Type
    a
  3. Lepsky, K.; Vorhauer, J.: Lingo - ein open source System für die Automatische Indexierung deutschsprachiger Dokumente (2006) 0.03 
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    Date
    24. 3.2006 12:22:02
    Type
    a
  4. Probst, M.; Mittelbach, J.: Maschinelle Indexierung in der Sacherschließung wissenschaftlicher Bibliotheken (2006) 0.03 
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    Date
    22. 3.2008 12:35:19
    Type
    a
  5. Newman, D.J.; Block, S.: Probabilistic topic decomposition of an eighteenth-century American newspaper (2006) 0.03 
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    Abstract
    We use a probabilistic mixture decomposition method to determine topics in the Pennsylvania Gazette, a major colonial U.S. newspaper from 1728-1800. We assess the value of several topic decomposition techniques for historical research and compare the accuracy and efficacy of various methods. After determining the topics covered by the 80,000 articles and advertisements in the entire 18th century run of the Gazette, we calculate how the prevalence of those topics changed over time, and give historically relevant examples of our findings. This approach reveals important information about the content of this colonial newspaper, and suggests the value of such approaches to a more complete understanding of early American print culture and society.
    Date
    22. 7.2006 17:32:00
    Type
    a
  6. Renz, M.: Automatische Inhaltserschließung im Zeichen von Wissensmanagement (2001) 0.02 
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    Date
    22. 3.2001 13:14:48
    Type
    a
  7. Lorenz, S.: Konzeption und prototypische Realisierung einer begriffsbasierten Texterschließung (2006) 0.01 
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    Date
    22. 3.2015 9:17:30
  8. Nohr, H.: Grundlagen der automatischen Indexierung : ein Lehrbuch (2003) 0.01 
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    Date
    22. 6.2009 12:46:51
  9. Hlava, M.M.: Automatic indexing : a matter of degree (2002) 0.00 
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    Type
    a
  10. Yusuff, A.: Automatisches Indexing and Abstracting : Grundlagen und Beispiele (2002) 0.00 
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    Imprint
    Potsdam : Fachhochschule, FB A-B-D
  11. Pulgarin, A.; Gil-Leiva, I.: Bibliometric analysis of the automatic indexing literature : 1956-2000 (2004) 0.00 
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    Abstract
    We present a bibliometric study of a corpus of 839 bibliographic references about automatic indexing, covering the period 1956-2000. We analyse the distribution of authors and works, the obsolescence and its dispersion, and the distribution of the literature by topic, year, and source type. We conclude that: (i) there has been a constant interest on the part of researchers; (ii) the most studied topics were the techniques and methods employed and the general aspects of automatic indexing; (iii) the productivity of the authors does fit a Lotka distribution (Dmax=0.02 and critical value=0.054); (iv) the annual aging factor is 95%; and (v) the dispersion of the literature is low.
    Type
    a
  12. Kantor, P.B.; Voorhees, E.: Information retrieval with scanned texts (2000) 0.00 
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    a
  13. Mongin, L.; Fu, Y.Y.; Mostafa, J.: Open Archives data Service prototype and automated subject indexing using D-Lib archive content as a testbed (2003) 0.00 
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    Abstract
    The Indiana University School of Library and Information Science opened a new research laboratory in January 2003; The Indiana University School of Library and Information Science Information Processing Laboratory [IU IP Lab]. The purpose of the new laboratory is to facilitate collaboration between scientists in the department in the areas of information retrieval (IR) and information visualization (IV) research. The lab has several areas of focus. These include grid and cluster computing, and a standard Java-based software platform to support plug and play research datasets, a selection of standard IR modules and standard IV algorithms. Future development includes software to enable researchers to contribute datasets, IR algorithms, and visualization algorithms into the standard environment. We decided early on to use OAI-PMH as a resource discovery tool because it is consistent with our mission.
    Type
    a
  14. Medelyan, O.; Witten, I.H.: Domain-independent automatic keyphrase indexing with small training sets (2008) 0.00 
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    Abstract
    Keyphrases are widely used in both physical and digital libraries as a brief, but precise, summary of documents. They help organize material based on content, provide thematic access, represent search results, and assist with navigation. Manual assignment is expensive because trained human indexers must reach an understanding of the document and select appropriate descriptors according to defined cataloging rules. We propose a new method that enhances automatic keyphrase extraction by using semantic information about terms and phrases gleaned from a domain-specific thesaurus. The key advantage of the new approach is that it performs well with very little training data. We evaluate it on a large set of manually indexed documents in the domain of agriculture, compare its consistency with a group of six professional indexers, and explore its performance on smaller collections of documents in other domains and of French and Spanish documents.
    Type
    a
  15. Woltering, H.: Maschinelle Indexierung in der Bibliothek der Friedrich-Ebert-Stiftung (2002) 0.00 
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    Type
    a
  16. Oberhauser, O.; Labner, J.: Einführung der automatischen Indexierung im Österreichischen Verbundkatalog? : Bericht über eine empirische Studie (2003) 0.00 
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    Location
    A
  17. Goller, C.; Löning, J.; Will, T.; Wolff, W.: Automatic document classification : a thourough evaluation of various methods (2000) 0.00 
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    Abstract
    (Automatic) document classification is generally defined as content-based assignment of one or more predefined categories to documents. Usually, machine learning, statistical pattern recognition, or neural network approaches are used to construct classifiers automatically. In this paper we thoroughly evaluate a wide variety of these methods on a document classification task for German text. We evaluate different feature construction and selection methods and various classifiers. Our main results are: (1) feature selection is necessary not only to reduce learning and classification time, but also to avoid overfitting (even for Support Vector Machines); (2) surprisingly, our morphological analysis does not improve classification quality compared to a letter 5-gram approach; (3) Support Vector Machines are significantly better than all other classification methods
    Type
    a
  18. Pirkola, A.: Morphological typology of languages for IR (2001) 0.00 
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    Abstract
    This paper presents a morphological classification of languages from the IR perspective. Linguistic typology research has shown that the morphological complexity of every language in the world can be described by two variables, index of synthesis and index of fusion. These variables provide a theoretical basis for IR research handling morphological issues. A common theoretical framework is needed in particular because of the increasing significance of cross-language retrieval research and CLIR systems processing different languages. The paper elaborates the linguistic morphological typology for the purposes of IR research. It studies how the indexes of synthesis and fusion could be used as practical tools in mono- and cross-lingual IR research. The need for semantic and syntactic typologies is discussed. The paper also reviews studies made in different languages on the effects of morphology and stemming in IR.
    Type
    a
  19. Bloomfield, M.: Indexing : neglected and poorly understood (2001) 0.00 
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    Abstract
    The growth of the Internet has highlighted the use of machine indexing. The difficulties in using the Internet as a searching device can be frustrating. The use of the term "Python" is given as an example. Machine indexing is noted as "rotten" and human indexing as "capricious." The problem seems to be a lack of a theoretical foundation for the art of indexing. What librarians have learned over the last hundred years has yet to yield a consistent approach to what really works best in preparing index terms and in the ability of our customers to search the various indexes. An attempt is made to consider the elements of indexing, their pros and cons. The argument is made that machine indexing is far too prolific in its production of index terms. Neither librarians nor computer programmers have made much progress to improve Internet indexing. Human indexing has had the same problems for over fifty years.
    Type
    a
  20. Mansour, N.; Haraty, R.A.; Daher, W.; Houri, M.: ¬An auto-indexing method for Arabic text (2008) 0.00 
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    Abstract
    This work addresses the information retrieval problem of auto-indexing Arabic documents. Auto-indexing a text document refers to automatically extracting words that are suitable for building an index for the document. In this paper, we propose an auto-indexing method for Arabic text documents. This method is mainly based on morphological analysis and on a technique for assigning weights to words. The morphological analysis uses a number of grammatical rules to extract stem words that become candidate index words. The weight assignment technique computes weights for these words relative to the container document. The weight is based on how spread is the word in a document and not only on its rate of occurrence. The candidate index words are then sorted in descending order by weight so that information retrievers can select the more important index words. We empirically verify the usefulness of our method using several examples. For these examples, we obtained an average recall of 46% and an average precision of 64%.
    Type
    a

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