-
Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003)
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- Abstract
- Rechnung, Kündigung oder Adressänderung? Eingehende Briefe und E-Mails werden immer häufiger von Software statt aufwändig von Menschenhand sortiert. Die Textklassifizierer arbeiten erstaunlich genau. Sie fahnden auch nach ähnlichen Texten und sorgen so für einen schnellen Überblick. Ihre Werkzeuge sind Linguistik, Statistik und Logik
-
Fangmeyer, H.; Gloden, R.: Bewertung und Vergleich von Klassifikationsergebnissen bei automatischen Verfahren (1978)
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- Source
- Kooperation in der Klassifikation I. Proc. der Sekt.1-3 der 2. Fachtagung der Gesellschaft für Klassifikation, Frankfurt-Hoechst, 6.-7.4.1978. Bearb.: W. Dahlberg
-
Illing, S.: Automatisiertes klinisches Codieren (2021)
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- Abstract
- Die in diesem Artikel vorgestellte Bachelorarbeit behandelt die Ergebnisse einer Shared Task im Bereich eHealth. Es wird untersucht, ob die Klassifikationsgenauigkeit ausgewählter klinischer Codiersysteme durch den Einsatz von Ensemble-Methoden verbessert werden kann. Entscheidend dafür sind die Werte der Evaluationsmaße Mean Average Precision und F1-Maß.
- Source
- Information - Wissenschaft und Praxis. 72(2021) H.5/6, S.285-290
-
Oberhauser, O.: Automatisches Klassifizieren und Bibliothekskataloge (2005)
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- Abstract
- In der bibliothekarischen Welt sind die Vorzüge einer klassifikatorischen Inhaltserschließung seit jeher wohlbekannt. Auch im Zeitalter der Online-Kataloge gibt es dafür keinen wirklichen Ersatz, da - kurz formuliert - ein stichwortbasiertes Retrieval alleine mit Problemen wie Ambiguität und Mehrsprachigkeit nicht fertig zu werden vermag. Zahlreiche Online-Kataloge weisen daher Notationen verschiedener Klassifikationssysteme auf; allerdings sind die darauf basierenden Abfragemöglichkeiten meist noch arg unterentwickelt. Viele Datensätze in OPACs sind aber überhaupt nicht sachlich erschlossen, sei es, dass sie aus retrospektiv konvertierten Nominalkatalogen stammen, sei es, dass ein Mangel an personellen Ressourcen ihre inhaltliche Erschließung verhindert hat. Angesichts großer Mengen solcher Datensätze liegt ein Interesse an automatischen Verfahren zur Sacherschließung durchaus nahe.
-
Greiner, G.: Intellektuelles und automatisches Klassifizieren (1981)
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-
Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003)
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- Footnote
- Auch unter: http://www.ariadne.ac.uk/issue37/lindholm/ und http://engine-e.lub.lu.se/
-
Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001)
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- Date
- 11. 5.2003 18:29:44
-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
-
Vizine-Goetz, D.: NetLab / OCLC collaboration seeks to improve Web searching (1999)
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- Abstract
- Vorstellung verschiedener Projekte zur Verbesserung der Internet-Erschließung mit Hilfe der DDC
-
HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016)
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- Date
- 1. 2.2016 18:25:22
-
Savic, D.: Designing an expert system for classifying office documents (1994)
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- Source
- Records management quarterly. 28(1994) no.3, S.20-29
-
Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995)
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- Source
- Records management quarterly. 29(1995) no.4, S.3-18
-
Ruocco, A.S.; Frieder, O.: Clustering and classification of large document bases in a parallel environment (1997)
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- Date
- 29. 7.1998 17:45:02
-
Cathey, R.J.; Jensen, E.C.; Beitzel, S.M.; Frieder, O.; Grossman, D.: Exploiting parallelism to support scalable hierarchical clustering (2007)
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- Abstract
- A distributed memory parallel version of the group average hierarchical agglomerative clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard Text REtrieval Conference (TREC) test collection, our parallel hierarchical clustering algorithm is shown to be scalable in terms of processors efficiently used and the collection size. Results show that our algorithm performs close to the expected O(n**2/p) time on p processors rather than the worst-case O(n**3/p) time. Furthermore, the O(n**2/p) memory complexity per node allows larger collections to be clustered as the number of nodes increases. While partitioning algorithms such as k-means are trivially parallelizable, our results confirm those of other studies which showed that hierarchical algorithms produce significantly tighter clusters in the document clustering task. Finally, we show how our parallel hierarchical agglomerative clustering algorithm can be used as the clustering subroutine for a parallel version of the buckshot algorithm to cluster the complete TREC collection at near theoretical runtime expectations.
-
Dubin, D.: Dimensions and discriminability (1998)
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- Date
- 22. 9.1997 19:16:05
-
Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998)
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- Date
- 1. 8.1996 22:08:06
-
Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006)
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- Date
- 22. 7.2006 16:24:52
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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Drori, O.; Alon, N.: Using document classification for displaying search results (2003)
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- Journal of information science. 29(2003) no.2, S.97-106
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Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003)
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- Source
- Journal of information science. 29(2003) no.2, S.117-126
