Search (13 results, page 1 of 1)

  • × theme_ss:"Automatisches Klassifizieren"
  • × theme_ss:"Internet"
  • × type_ss:"a"
  1. Egbert, J.; Biber, D.; Davies, M.: Developing a bottom-up, user-based method of web register classification (2015) 0.02 
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    Abstract
    This paper introduces a project to develop a reliable, cost-effective method for classifying Internet texts into register categories, and apply that approach to the analysis of a large corpus of web documents. To date, the project has proceeded in 2 key phases. First, we developed a bottom-up method for web register classification, asking end users of the web to utilize a decision-tree survey to code relevant situational characteristics of web documents, resulting in a bottom-up identification of register and subregister categories. We present details regarding the development and testing of this method through a series of 10 pilot studies. Then, in the second phase of our project we applied this procedure to a corpus of 53,000 web documents. An analysis of the results demonstrates the effectiveness of these methods for web register classification and provides a preliminary description of the types and distribution of registers on the web.
    Date
    4. 8.2015 19:22:04
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.9, S.1817-1831
    Type
    a
  2. Möller, G.: Automatic classification of the World Wide Web using Universal Decimal Classification (1999) 0.01 
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    Imprint
    Hinskey Hill : Learned Information
    Source
    Online information 99: 23rd International Online Information Meeting, Proceedings, London, 7-9 December 1999. Ed.: D. Raitt et al
    Type
    a
  3. Chung, Y.-M.; Noh, Y.-H.: Developing a specialized directory system by automatically classifying Web documents (2003) 0.01 
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    Abstract
    This study developed a specialized directory system using an automatic classification technique. Economics was selected as the subject field for the classification experiments with Web documents. The classification scheme of the directory follows the DDC, and subject terms representing each class number or subject category were selected from the DDC table to construct a representative term dictionary. In collecting and classifying the Web documents, various strategies were tested in order to find the optimal thresholds. In the classification experiments, Web documents in economics were classified into a total of 757 hierarchical subject categories built from the DDC scheme. The first and second experiments using the representative term dictionary resulted in relatively high precision ratios of 77 and 60%, respectively. The third experiment employing a machine learning-based k-nearest neighbours (kNN) classifier in a closed experimental setting achieved a precision ratio of 96%. This implies that it is possible to enhance the classification performance by applying a hybrid method combining a dictionary-based technique and a kNN classifier
    Source
    Journal of information science. 29(2003) no.2, S.117-126
    Type
    a
  4. McKiernan, G.: Automated categorisation of Web resources : a profile of selected projects, research, products, and services (1996) 0.01 
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    Source
    New review of information networking. 1996, no.2, S.15-40
    Type
    a
  5. Koch, T.: Experiments with automatic classification of WAIS databases and indexing of WWW : some results from the Nordic WAIS/WWW project (1994) 0.01 
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    Abstract
    The Nordic WAIS/WWW project sponsored by NORDINFO is a joint project between Lund University Library and the National Technological Library of Denmark. It aims to improve the existing networked information discovery and retrieval tools Wide Area Information System (WAIS) and World Wide Web (WWW), and to move towards unifying WWW and WAIS. Details current results focusing on the WAIS side of the project. Describes research into automatic indexing and classification of WAIS sources, development of an orientation tool for WAIS, and development of a WAIS index of WWW resources
    Type
    a
  6. Choi, B.; Peng, X.: Dynamic and hierarchical classification of Web pages (2004) 0.01 
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    Abstract
    Automatic classification of Web pages is an effective way to organise the vast amount of information and to assist in retrieving relevant information from the Internet. Although many automatic classification systems have been proposed, most of them ignore the conflict between the fixed number of categories and the growing number of Web pages being added into the systems. They also require searching through all existing categories to make any classification. This article proposes a dynamic and hierarchical classification system that is capable of adding new categories as required, organising the Web pages into a tree structure, and classifying Web pages by searching through only one path of the tree. The proposed single-path search technique reduces the search complexity from (n) to (log(n)). Test results show that the system improves the accuracy of classification by 6 percent in comparison to related systems. The dynamic-category expansion technique also achieves satisfying results for adding new categories into the system as required.
    Source
    Online information review. 28(2004) no.2, S.139-147
    Type
    a
  7. GERHARD : eine Spezialsuchmaschine für die Wissenschaft (1998) 0.00 
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  8. Chan, L.M.; Lin, X.; Zeng, M.L.: Structural and multilingual approaches to subject access on the Web (2000) 0.00 
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  9. Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001) 0.00 
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  10. Vizine-Goetz, D.: NetLab / OCLC collaboration seeks to improve Web searching (1999) 0.00 
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  11. Shafer, K.E.: Evaluating Scorpion Results (2001) 0.00 
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  12. Koch, T.: Nutzung von Klassifikationssystemen zur verbesserten Beschreibung, Organisation und Suche von Internetressourcen (1998) 0.00 
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  13. Wätjen, H.-J.: GERHARD : Automatisches Sammeln, Klassifizieren und Indexieren von wissenschaftlich relevanten Informationsressourcen im deutschen World Wide Web (1998) 0.00 
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  • e 10
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