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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
-
Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007)
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- Date
- 6. 1.1997 18:30:28
28. 2.2008 14:21:51
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Malenica, M.; Smuc, T.; Snajder, J.; Basic, B.D.: Language morphology offset : text classification on a Croatian-English parallel corpus (2008)
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- Date
- 28. 7.2008 15:54:17
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Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007)
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- Source
- Journal of intelligent information systems. 29(2007) no.2, S.211-230
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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
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Savic, D.: Automatic classification of office documents : review of available methods and techniques (1995)
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- Date
- 23. 7.1996 10:28:09
- Source
- Records management quarterly. 29(1995) no.4, S.3-18
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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- Date
- 28. 9.2003 11:42:17
22. 9.2008 18:31:54
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Ardö, A.; Koch, T.: Automatic classification applied to full-text Internet documents in a robot-generated subject index (1999)
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Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009)
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- Date
- 22. 8.2009 19:51:28
-
Braun, T.: Dokumentklassifikation durch Clustering (o.J.)
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Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002)
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- Date
- 6. 1.1997 18:30:28
- Source
- Knowledge organization. 29(2002) nos.3/4, S.181-197
-
Giorgetti, D.; Sebastiani, F.: Automating survey coding by multiclass text categorization techniques (2003)
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- Abstract
- In this issue Giorgetti, and Sebastiani suggest that answers to open ended questions in survey instruments can be coded automatically by creating classifiers which learn from training sets of manually coded answers. The manual effort required is only that of classifying a representative set of documents, not creating a dictionary of words that trigger an assignment. They use a naive Bayesian probabilistic learner from Mc Callum's RAINBOW package and the multi-class support vector machine learner from Hsu and Lin's BSVM package, both examples of text categorization techniques. Data from the 1996 General Social Survey by the U.S. National Opinion Research Center provided a set of answers to three questions (previously tested by Viechnicki using a dictionary approach), their associated manually assigned category codes, and a complete set of predefined category codes. The learners were run on three random disjoint subsets of the answer sets to create the classifiers and a remaining set was used as a test set. The dictionary approach is out preformed by 18% for RAINBOW and by 17% for BSVM, while the standard deviation of the results is reduced by 28% and 34% respectively over the dictionary approach.
- Date
- 9. 7.2006 10:29:12
-
Liu, R.-L.: Context-based term frequency assessment for text classification (2010)
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- Abstract
- Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.
-
Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998)
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Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000)
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Brückner, T.; Dambeck, H.: Sortierautomaten : Grundlagen der Textklassifizierung (2003)
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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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Pfister, J.: Clustering von Patent-Dokumenten am Beispiel der Datenbanken des Fachinformationszentrums Karlsruhe (2006)
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- Date
- 6. 1.1997 18:30:28
12. 9.2004 9:56:22
