Search (57 results, page 2 of 3)

Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.01

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Date: 28.10.2013 19:22:57

Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.01

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Chan, L.M.; Lin, X.; Zeng, M.: Structural and multilingual approaches to subject access on the Web (1999) 0.01

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Zhang, X: Rough set theory based automatic text categorization (2005) 0.01

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Type: m

Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.01

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Date: 22. 1.2010 14:41:24

Ahmed, M.; Mukhopadhyay, M.; Mukhopadhyay, P.: Automated knowledge organization : AI ML based subject indexing system for libraries (2023) 0.01

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Mostafa, J.; Quiroga, L.M.; Palakal, M.: Filtering medical documents using automated and human classification methods (1998) 0.00

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Koch, T.; Vizine-Goetz, D.: DDC and knowledge organization in the digital library : Research and development. Demonstration pages (1999) 0.00

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Footnote: Vortrag anläßlich des Workshops am 21.10.1999, Deutsche Bibliothek, Frankfurt/M.

Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.00

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Mukhopadhyay, S.; Peng, S.; Raje, R.; Palakal, M.; Mostafa, J.: Multi-agent information classification using dynamic acquaintance lists (2003) 0.00

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Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004) 0.00

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Prabowo, R.; Jackson, M.; Burden, P.; Knoell, H.-D.: Ontology-based automatic classification for the Web pages : design, implementation and evaluation (2002) 0.00

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Duwairi, R.M.: Machine learning for Arabic text categorization (2006) 0.00
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```
Abstract

In this article we propose a distance-based classifier for categorizing Arabic text. Each category is represented as a vector of words in an m-dimensional space, and documents are classified on the basis of their closeness to feature vectors of categories. The classifier, in its learning phase, scans the set of training documents to extract features of categories that capture inherent category-specific properties; in its testing phase the classifier uses previously determined category-specific features to categorize unclassified documents. Stemming was used to reduce the dimensionality of feature vectors of documents. The accuracy of the classifier was tested by carrying out several categorization tasks on an in-house collected Arabic corpus. The results show that the proposed classifier is very accurate and robust.

Hung, C.-M.; Chien, L.-F.: Web-based text classification in the absence of manually labeled training documents (2007) 0.00

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Pfeffer, M.: Automatische Vergabe von RVK-Notationen anhand von bibliografischen Daten mittels fallbasiertem Schließen (2007) 0.00

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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) 0.00

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Zhou, G.D.; Zhang, M.; Ji, D.H.; Zhu, Q.M.: Hierarchical learning strategy in semantic relation extraction (2008) 0.00

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Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008) 0.00

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Cortez, E.; Herrera, M.R.; Silva, A.S. da; Moura, E.S. de; Neubert, M.: Lightweight methods for large-scale product categorization (2011) 0.00

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Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.00

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