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  1. Ingwersen, P.; Wormell, I.: Ranganathan in the perspective of advanced information retrieval (1992) 0.00 
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  2. Fuhr, N.: Klassifikationsverfahren bei der automatischen Indexierung (1983) 0.00 
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  3. Krauth, J.: Evaluation von Verfahren der automatischen Klassifikation (1983) 0.00 
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  4. Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998) 0.00 
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  5. Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003) 0.00 
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  6. Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006) 0.00 
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  7. Illing, S.: Automatisiertes klinisches Codieren (2021) 0.00 
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  8. Golub, K.: Automated subject classification of textual web documents (2006) 0.00 
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    Abstract
    Purpose - To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such. Design/methodology/approach - A range of works dealing with automated classification of full-text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages. Findings - Provides major similarities and differences between the three approaches: document pre-processing and utilization of web-specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized. Research limitations/implications - The paper does not attempt to provide an exhaustive bibliography of related resources. Practical implications - As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities. Originality/value - To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.
    Type
    a
  9. HaCohen-Kerner, Y.; Beck, H.; Yehudai, E.; Rosenstein, M.; Mughaz, D.: Cuisine : classification using stylistic feature sets and/or name-based feature sets (2010) 0.00 
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    Abstract
    Document classification presents challenges due to the large number of features, their dependencies, and the large number of training documents. In this research, we investigated the use of six stylistic feature sets (including 42 features) and/or six name-based feature sets (including 234 features) for various combinations of the following classification tasks: ethnic groups of the authors and/or periods of time when the documents were written and/or places where the documents were written. The investigated corpus contains Jewish Law articles written in Hebrew-Aramaic, which present interesting problems for classification. Our system CUISINE (Classification UsIng Stylistic feature sets and/or NamE-based feature sets) achieves accuracy results between 90.71 to 98.99% for the seven classification experiments (ethnicity, time, place, ethnicity&time, ethnicity&place, time&place, ethnicity&time&place). For the first six tasks, the stylistic feature sets in general and the quantitative feature set in particular are enough for excellent classification results. In contrast, the name-based feature sets are rather poor for these tasks. However, for the most complex task (ethnicity&time&place), a hill-climbing model using all feature sets succeeds in significantly improving the classification results. Most of the stylistic features (34 of 42) are language-independent and domain-independent. These features might be useful to the community at large, at least for rather simple tasks.
    Type
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  10. Panyr, J.: Automatische thematische Textklassifikation und ihre Interpretation in der Dokumentengrobrecherche (1980) 0.00 
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  11. Wätjen, H.-J.: GERHARD : Automatisches Sammeln, Klassifizieren und Indexieren von wissenschaftlich relevanten Informationsressourcen im deutschen World Wide Web (1998) 0.00 
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  12. Oberhauser, O.: Automatisches Klassifizieren und Bibliothekskataloge (2005) 0.00 
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  13. Cui, H.; Heidorn, P.B.; Zhang, H.: ¬An approach to automatic classification of text for information retrieval (2002) 0.00 
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  14. Reiner, U.: DDC-based search in the data of the German National Bibliography (2008) 0.00 
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  15. Automatische Klassifikation und Extraktion in Documentum (2005) 0.00 
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  16. Schek, M.: Automatische Klassifizierung in Erschließung und Recherche eines Pressearchivs (2006) 0.00 
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  17. Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010) 0.00 
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  18. Schek, M.: Automatische Klassifizierung und Visualisierung im Archiv der Süddeutschen Zeitung (2005) 0.00 
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