Search (278 results, page 2 of 14)

  • × theme_ss:"Automatisches Indexieren"
  1. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.02
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    Language
    e
    Source
    Cataloging and classification quarterly. 60(2022) no.8, p.807-835
  2. Kanan, T.; Fox, E.A.: Automated arabic text classification with P-Stemmer, machine learning, and a tailored news article taxonomy (2016) 0.02
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    Abstract
    Arabic news articles in electronic collections are difficult to study. Browsing by category is rarely supported. Although helpful machine-learning methods have been applied successfully to similar situations for English news articles, limited research has been completed to yield suitable solutions for Arabic news. In connection with a Qatar National Research Fund (QNRF)-funded project to build digital library community and infrastructure in Qatar, we developed software for browsing a collection of about 237,000 Arabic news articles, which should be applicable to other Arabic news collections. We designed a simple taxonomy for Arabic news stories that is suitable for the needs of Qatar and other nations, is compatible with the subject codes of the International Press Telecommunications Council, and was enhanced with the aid of a librarian expert as well as five Arabic-speaking volunteers. We developed tailored stemming (i.e., a new Arabic light stemmer called P-Stemmer) and automatic classification methods (the best being binary Support Vector Machines classifiers) to work with the taxonomy. Using evaluation techniques commonly used in the information retrieval community, including 10-fold cross-validation and the Wilcoxon signed-rank test, we showed that our approach to stemming and classification is superior to state-of-the-art techniques.
    Language
    e
  3. Hodges, P.R.: Keyword in title indexes : effectiveness of retrieval in computer searches (1983) 0.02
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    Date
    14. 3.1996 13:22:21
    Language
    e
  4. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.02
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    Language
    e
    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28
  5. Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997) 0.02
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    Date
    1. 8.1996 22:08:06
    Language
    e
  6. Newman, D.J.; Block, S.: Probabilistic topic decomposition of an eighteenth-century American newspaper (2006) 0.02
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    Date
    22. 7.2006 17:32:00
    Language
    e
  7. Cui, H.; Boufford, D.; Selden, P.: Semantic annotation of biosystematics literature without training examples (2010) 0.02
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    Abstract
    This article presents an unsupervised algorithm for semantic annotation of morphological descriptions of whole organisms. The algorithm is able to annotate plain text descriptions with high accuracy at the clause level by exploiting the corpus itself. In other words, the algorithm does not need lexicons, syntactic parsers, training examples, or annotation templates. The evaluation on two real-life description collections in botany and paleontology shows that the algorithm has the following desirable features: (a) reduces/eliminates manual labor required to compile dictionaries and prepare source documents; (b) improves annotation coverage: the algorithm annotates what appears in documents and is not limited by predefined and often incomplete templates; (c) learns clean and reusable concepts: the algorithm learns organ names and character states that can be used to construct reusable domain lexicons, as opposed to collection-dependent patterns whose applicability is often limited to a particular collection; (d) insensitive to collection size; and (e) runs in linear time with respect to the number of clauses to be annotated.
    Language
    e
  8. Oberhauser, O.; Labner, J.: Einführung der automatischen Indexierung im Österreichischen Verbundkatalog? : Bericht über eine empirische Studie (2003) 0.02
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    Type
    p
  9. Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018) 0.02
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  10. Matthews, P.; Glitre, K.: Genre analysis of movies using a topic model of plot summaries (2021) 0.02
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  11. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.02
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    Language
    e
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.815-834
  12. Asula, M.; Makke, J.; Freienthal, L.; Kuulmets, H.-A.; Sirel, R.: Kratt: developing an automatic subject indexing tool for the National Library of Estonia : how to transfer metadata information among work cluster members (2021) 0.02
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    Language
    e
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.775-793
  13. Ward, M.L.: ¬The future of the human indexer (1996) 0.01
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    Date
    9. 2.1997 18:44:22
    Language
    e
  14. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  15. Strobel, S.; Marín-Arraiza, P.: Metadata for scientific audiovisual media : current practices and perspectives of the TIB / AV-portal (2015) 0.01
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    Source
    Metadata and semantics research: 9th Research Conference, MTSR 2015, Manchester, UK, September 9-11, 2015, Proceedings. Eds.: E. Garoufallou et al
  16. Schäuble, P.: Kostengünstige Konversion großer Bibliothekskataloge (1996) 0.01
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  17. Rasmussen, E.M.: Indexing and retrieval for the Web (2002) 0.01
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    Abstract
    The introduction and growth of the World Wide Web (WWW, or Web) have resulted in a profound change in the way individuals and organizations access information. In terms of volume, nature, and accessibility, the characteristics of electronic information are significantly different from those of even five or six years ago. Control of, and access to, this flood of information rely heavily an automated techniques for indexing and retrieval. According to Gudivada, Raghavan, Grosky, and Kasanagottu (1997, p. 58), "The ability to search and retrieve information from the Web efficiently and effectively is an enabling technology for realizing its full potential." Almost 93 percent of those surveyed consider the Web an "indispensable" Internet technology, second only to e-mail (Graphie, Visualization & Usability Center, 1998). Although there are other ways of locating information an the Web (browsing or following directory structures), 85 percent of users identify Web pages by means of a search engine (Graphie, Visualization & Usability Center, 1998). A more recent study conducted by the Stanford Institute for the Quantitative Study of Society confirms the finding that searching for information is second only to e-mail as an Internet activity (Nie & Ebring, 2000, online). In fact, Nie and Ebring conclude, "... the Internet today is a giant public library with a decidedly commercial tilt. The most widespread use of the Internet today is as an information search utility for products, travel, hobbies, and general information. Virtually all users interviewed responded that they engaged in one or more of these information gathering activities."
    Techniques for automated indexing and information retrieval (IR) have been developed, tested, and refined over the past 40 years, and are well documented (see, for example, Agosti & Smeaton, 1996; BaezaYates & Ribeiro-Neto, 1999a; Frakes & Baeza-Yates, 1992; Korfhage, 1997; Salton, 1989; Witten, Moffat, & Bell, 1999). With the introduction of the Web, and the capability to index and retrieve via search engines, these techniques have been extended to a new environment. They have been adopted, altered, and in some Gases extended to include new methods. "In short, search engines are indispensable for searching the Web, they employ a variety of relatively advanced IR techniques, and there are some peculiar aspects of search engines that make searching the Web different than more conventional information retrieval" (Gordon & Pathak, 1999, p. 145). The environment for information retrieval an the World Wide Web differs from that of "conventional" information retrieval in a number of fundamental ways. The collection is very large and changes continuously, with pages being added, deleted, and altered. Wide variability between the size, structure, focus, quality, and usefulness of documents makes Web documents much more heterogeneous than a typical electronic document collection. The wide variety of document types includes images, video, audio, and scripts, as well as many different document languages. Duplication of documents and sites is common. Documents are interconnected through networks of hyperlinks. Because of the size and dynamic nature of the Web, preprocessing all documents requires considerable resources and is often not feasible, certainly not an the frequent basis required to ensure currency. Query length is usually much shorter than in other environments-only a few words-and user behavior differs from that in other environments. These differences make the Web a novel environment for information retrieval (Baeza-Yates & Ribeiro-Neto, 1999b; Bharat & Henzinger, 1998; Huang, 2000).
    Language
    e
  18. Ahlgren, P.; Kekäläinen, J.: Indexing strategies for Swedish full text retrieval under different user scenarios (2007) 0.01
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  19. Humphrey, S.M.; Névéol, A.; Browne, A.; Gobeil, J.; Ruch, P.; Darmoni, S.J.: Comparing a rule-based versus statistical system for automatic categorization of MEDLINE documents according to biomedical specialty (2009) 0.01
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  20. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.01
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    Date
    14. 6.2015 22:12:44

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