Search (80 results, page 4 of 4)

  • × theme_ss:"Automatisches Klassifizieren"
  1. 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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  2. 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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  3. 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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  4. Li, T.; Zhu, S.; Ogihara, M.: Hierarchical document classification using automatically generated hierarchy (2007) 0.00 
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  5. Sommer, M.: Automatische Generierung von DDC-Notationen für Hochschulveröffentlichungen (2012) 0.00 
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  6. Wu, M.; Liu, Y.-H.; Brownlee, R.; Zhang, X.: Evaluating utility and automatic classification of subject metadata from Research Data Australia (2021) 0.00 
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  7. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.00 
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    Date
    22. 3.2009 19:11:54
  8. Zhu, W.Z.; Allen, R.B.: Document clustering using the LSI subspace signature model (2013) 0.00 
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    Date
    23. 3.2013 13:22:36
  9. Calado, P.; Cristo, M.; Gonçalves, M.A.; Moura, E.S. de; Ribeiro-Neto, B.; Ziviani, N.: Link-based similarity measures for the classification of Web documents (2006) 0.00 
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  10. Rooney, N.; Patterson, D.; Galushka, M.; Dobrynin, V.; Smirnova, E.: ¬An investigation into the stability of contextual document clustering (2008) 0.00 
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  11. Li, T.; Zhu, S.; Ogihara, M.: Text categorization via generalized discriminant analysis (2008) 0.00 
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  12. Golub, K.; Lykke, M.: Automated classification of web pages in hierarchical browsing (2009) 0.00 
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  13. 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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  14. Wartena, C.; Sommer, M.: Automatic classification of scientific records using the German Subject Heading Authority File (SWD) (2012) 0.00 
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  15. Wang, H.; Hong, M.: Supervised Hebb rule based feature selection for text classification (2019) 0.00 
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  16. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.00 
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    Date
    22. 3.2009 19:14:43
  17. Liu, R.-L.: ¬A passage extractor for classification of disease aspect information (2013) 0.00 
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    Date
    28.10.2013 19:22:57
  18. 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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  19. Kragelj, M.; Borstnar, M.K.: Automatic classification of older electronic texts into the Universal Decimal Classification-UDC (2021) 0.00 
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  20. Borko, H.: Research in computer based classification systems (1985) 0.00 
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    Abstract
    The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.

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