-
Drori, O.; Alon, N.: Using document classification for displaying search results (2003)
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- Source
- Journal of information science. 29(2003) no.2, S.97-106
- Year
- 2003
-
Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003)
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- Journal of information science. 29(2003) no.2, S.117-126
- Year
- 2003
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Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003)
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- Date
- 9. 7.2006 10:29:12
- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.14, S.1269-1277
- Year
- 2003
-
Kwon, O.W.; Lee, J.H.: Text categorization based on k-nearest neighbor approach for web site classification (2003)
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- Date
- 27.12.2007 17:32:29
- Source
- Information processing and management. 39(2003) no.1, S.25-44
- Year
- 2003
-
Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003)
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- Source
- c't. 2003, H.19, S.192-197
- Year
- 2003
-
Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003)
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- Source
- Ariadne magazine. 2003, no.37
- Year
- 2003
-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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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
-
Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003)
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- Date
- 12. 9.2004 9:56:22
- Year
- 2003
-
Miyamoto, S.: Information clustering based an fuzzy multisets (2003)
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- Source
- Information processing and management. 39(2003) no.2, S.195-213
- Year
- 2003
-
Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.10, S.966-975
- Year
- 2003
-
Sun, A.; Lim, E.-P.; Ng, W.-K.: Performance measurement framework for hierarchical text classification (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.11, S.1014-1028
- Year
- 2003
-
Godby, C.J.; Stuler, J.: ¬The Library of Congress Classification as a knowledge base for automatic subject categorization : subject access issues (2003)
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- Year
- 2003
-
Zhang, X: Rough set theory based automatic text categorization (2005)
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- Abstract
- Der Forschungsbericht "Rough Set Theory Based Automatic Text Categorization and the Handling of Semantic Heterogeneity" von Xueying Zhang ist in Buchform auf Englisch erschienen. Zhang hat in ihrer Arbeit ein Verfahren basierend auf der Rough Set Theory entwickelt, das Beziehungen zwischen Schlagwörtern verschiedener Vokabulare herstellt. Sie war von 2003 bis 2005 Mitarbeiterin des IZ und ist seit Oktober 2005 Associate Professor an der Nanjing University of Science and Technology.
-
Adams, K.C.: Word wranglers : Automatic classification tools transform enterprise documents from "bags of words" into knowledge resources (2003)
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- Year
- 2003
-
Zhou, G.D.; Zhang, M.; Ji, D.H.; Zhu, Q.M.: Hierarchical learning strategy in semantic relation extraction (2008)
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- Abstract
- This paper proposes a novel hierarchical learning strategy to deal with the data sparseness problem in semantic relation extraction by modeling the commonality among related classes. For each class in the hierarchy either manually predefined or automatically clustered, a discriminative function is determined in a top-down way. As the upper-level class normally has much more positive training examples than the lower-level class, the corresponding discriminative function can be determined more reliably and guide the discriminative function learning in the lower-level one more effectively, which otherwise might suffer from limited training data. In this paper, two classifier learning approaches, i.e. the simple perceptron algorithm and the state-of-the-art Support Vector Machines, are applied using the hierarchical learning strategy. Moreover, several kinds of class hierarchies either manually predefined or automatically clustered are explored and compared. Evaluation on the ACE RDC 2003 and 2004 corpora shows that the hierarchical learning strategy much improves the performance on least- and medium-frequent relations.
-
Reiner, U.: VZG-Projekt Colibri : Bewertung von automatisch DDC-klassifizierten Titeldatensätzen der Deutschen Nationalbibliothek (DNB) (2009)
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- Abstract
- Das VZG-Projekt Colibri/DDC beschäftigt sich seit 2003 mit automatischen Verfahren zur Dewey-Dezimalklassifikation (Dewey Decimal Classification, kurz DDC). Ziel des Projektes ist eine einheitliche DDC-Erschließung von bibliografischen Titeldatensätzen und eine Unterstützung der DDC-Expert(inn)en und DDC-Laien, z. B. bei der Analyse und Synthese von DDC-Notationen und deren Qualitätskontrolle und der DDC-basierten Suche. Der vorliegende Bericht konzentriert sich auf die erste größere automatische DDC-Klassifizierung und erste automatische und intellektuelle Bewertung mit der Klassifizierungskomponente vc_dcl1. Grundlage hierfür waren die von der Deutschen Nationabibliothek (DNB) im November 2007 zur Verfügung gestellten 25.653 Titeldatensätze (12 Wochen-/Monatslieferungen) der Deutschen Nationalbibliografie der Reihen A, B und H. Nach Erläuterung der automatischen DDC-Klassifizierung und automatischen Bewertung in Kapitel 2 wird in Kapitel 3 auf den DNB-Bericht "Colibri_Auswertung_DDC_Endbericht_Sommer_2008" eingegangen. Es werden Sachverhalte geklärt und Fragen gestellt, deren Antworten die Weichen für den Verlauf der weiteren Klassifizierungstests stellen werden. Über das Kapitel 3 hinaus führende weitergehende Betrachtungen und Gedanken zur Fortführung der automatischen DDC-Klassifizierung werden in Kapitel 4 angestellt. Der Bericht dient dem vertieften Verständnis für die automatischen Verfahren.
-
Panyr, J.: STEINADLER: ein Verfahren zur automatischen Deskribierung und zur automatischen thematischen Klassifikation (1978)
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- Source
- Nachrichten für Dokumentation. 29(1978), S.92-96
-
Schek, M.: Automatische Klassifizierung und Visualisierung im Archiv der Süddeutschen Zeitung (2005)
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- Abstract
- DIZ definiert das Wissensnetz als Alleinstellungsmerkmal und wendet beträchtliche personelle Ressourcen für die Aktualisierung und Oualitätssicherung der Dossiers auf. Nach der Umstellung auf den komplett digitalisierten Workflow im April 2001 identifizierte DIZ vier Ansatzpunkte, wie die Aufwände auf der Inputseite (Lektorat) zu optimieren sind und gleichzeitig auf der Outputseite (Recherche) das Wissensnetz besser zu vermarkten ist: 1. (Teil-)Automatische Klassifizierung von Pressetexten (Vorschlagwesen) 2. Visualisierung des Wissensnetzes (Topic Mapping) 3. (Voll-)Automatische Klassifizierung und Optimierung des Wissensnetzes 4. Neue Retrievalmöglichkeiten (Clustering, Konzeptsuche) Die Projekte 1 und 2 "Automatische Klassifizierung und Visualisierung" starteten zuerst und wurden beschleunigt durch zwei Entwicklungen: - Der Bayerische Rundfunk (BR), ursprünglich Mitbegründer und 50%-Gesellschafter der DIZ München GmbH, entschloss sich aus strategischen Gründen, zum Ende 2003 aus der Kooperation auszusteigen. - Die Medienkrise, hervorgerufen durch den massiven Rückgang der Anzeigenerlöse, erforderte auch im Süddeutschen Verlag massive Einsparungen und die Suche nach neuen Erlösquellen. Beides führte dazu, dass die Kapazitäten im Bereich Pressedokumentation von ursprünglich rund 20 (nur SZ, ohne BR-Anteil) auf rund 13 zum 1. Januar 2004 sanken und gleichzeitig die Aufwände für die Pflege des Wissensnetzes unter verstärkten Rechtfertigungsdruck gerieten. Für die Projekte 1 und 2 ergaben sich daraus drei quantitative und qualitative Ziele: - Produktivitätssteigerung im Lektorat - Konsistenzverbesserung im Lektorat - Bessere Vermarktung und intensivere Nutzung der Dossiers in der Recherche Alle drei genannten Ziele konnten erreicht werden, wobei insbesondere die Produktivität im Lektorat gestiegen ist. Die Projekte 1 und 2 "Automatische Klassifizierung und Visualisierung" sind seit Anfang 2004 erfolgreich abgeschlossen. Die Folgeprojekte 3 und 4 laufen seit Mitte 2004 und sollen bis Mitte 2005 abgeschlossen sein. Im folgenden wird in Abschnitt 2 die Produktauswahl und Arbeitsweise der Automatischen Klassifizierung beschrieben. Abschnitt 3 schildert den Einsatz der Wissensnetz-Visualisierung in Lektorat und Recherche. Abschnitt 4 fasst die Ergebnisse der Projekte 1 und 2 zusammen und gibt einen Ausblick auf die Ziele der Projekte 3 und 4.
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Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
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Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009)
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- Date
- 22. 8.2009 12:54:24
