Search (6 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × type_ss:"el"
  • × year_i:[2000 TO 2010}
  1. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01 
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    Date
    22. 8.2009 12:54:24
  2. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
  3. Reiner, U.: Automatische DDC-Klassifizierung bibliografischer Titeldatensätze der Deutschen Nationalbibliografie (2009) 0.00 
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    Date
    22. 1.2010 14:41:24
  4. Adams, K.C.: Word wranglers : Automatic classification tools transform enterprise documents from "bags of words" into knowledge resources (2003) 0.00 
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    Abstract
    Taxonomies are an important part of any knowledge management (KM) system, and automatic classification software is emerging as a "killer app" for consumer and enterprise portals. A number of companies such as Inxight Software , Mohomine, Metacode, and others claim to interpret the semantic content of any textual document and automatically classify text on the fly. The promise that software could automatically produce a Yahoo-style directory is a siren call not many IT managers are able to resist. KM needs have grown more complex due to the increasing amount of digital information, the declining effectiveness of keyword searching, and heterogeneous document formats in corporate databases. This environment requires innovative KM tools, and automatic classification technology is an example of this new kind of software. These products can be divided into three categories according to their underlying technology - rules-based, catalog-by-example, and statistical clustering. Evolving trends in this market include framing classification as a cyborg (computer- and human-based) activity and the increasing use of extensible markup language (XML) and support vector machine (SVM) technology. In this article, we'll survey the rapidly changing automatic classification software market and examine the features and capabilities of leading classification products.
  5. 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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  6. Hagedorn, K.; Chapman, S.; Newman, D.: Enhancing search and browse using automated clustering of subject metadata (2007) 0.00 
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    Abstract
    The Web puzzle of online information resources often hinders end-users from effective and efficient access to these resources. Clustering resources into appropriate subject-based groupings may help alleviate these difficulties, but will it work with heterogeneous material? The University of Michigan and the University of California Irvine joined forces to test automatically enhancing metadata records using the Topic Modeling algorithm on the varied OAIster corpus. We created labels for the resulting clusters of metadata records, matched the clusters to an in-house classification system, and developed a prototype that would showcase methods for search and retrieval using the enhanced records. Results indicated that while the algorithm was somewhat time-intensive to run and using a local classification scheme had its drawbacks, precise clustering of records was achieved and the prototype interface proved that faceted classification could be powerful in helping end-users find resources.

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