Search (83 results, page 1 of 5)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  • × year_i:[2000 TO 2010}
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.05 
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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
    Language
    e
  2. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
    Language
    e
  3. Ibekwe-SanJuan, F.; SanJuan, E.: From term variants to research topics (2002) 0.01 
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    Abstract
    In a scientific and technological watch (STW) task, an expert user needs to survey the evolution of research topics in his area of specialisation in order to detect interesting changes. The majority of methods proposing evaluation metrics (bibliometrics and scientometrics studies) for STW rely solely an statistical data analysis methods (Co-citation analysis, co-word analysis). Such methods usually work an structured databases where the units of analysis (words, keywords) are already attributed to documents by human indexers. The advent of huge amounts of unstructured textual data has rendered necessary the integration of natural language processing (NLP) techniques to first extract meaningful units from texts. We propose a method for STW which is NLP-oriented. The method not only analyses texts linguistically in order to extract terms from them, but also uses linguistic relations (syntactic variations) as the basis for clustering. Terms and variation relations are formalised as weighted di-graphs which the clustering algorithm, CPCL (Classification by Preferential Clustered Link) will seek to reduce in order to produces classes. These classes ideally represent the research topics present in the corpus. The results of the classification are subjected to validation by an expert in STW.
    Language
    e
  4. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
    Language
    e
  5. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01 
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    Date
    22. 7.2006 16:24:52
    Language
    e
  6. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01 
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    Date
    22. 9.2008 18:31:54
    Language
    e
  7. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
    Language
    e
  8. Mengle, S.; Goharian, N.: Passage detection using text classification (2009) 0.01 
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    Date
    22. 3.2009 19:14:43
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  9. Khoo, C.S.G.; Ng, K.; Ou, S.: ¬An exploratory study of human clustering of Web pages (2003) 0.01 
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    Date
    12. 9.2004 9:56:22
    Language
    e
  10. Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001) 0.00 
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  11. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00 
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  12. Major, R.L.; Ragsdale, C.T.: ¬An aggregation approach to the classification problem using multiple prediction experts (2000) 0.00 
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  13. 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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  14. Shafer, K.E.: Automatic Subject Assignment via the Scorpion System (2001) 0.00 
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  15. Yu, W.; Gong, Y.: Document clustering by concept factorization (2004) 0.00 
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  16. Reiner, U.: Automatic analysis of DDC notations (2007) 0.00 
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  17. Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003) 0.00 
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    Abstract
    Authors describe the background and the work involved in setting up Engine-e, a Web index that uses automatic classification as a mean for the selection of resources in Engineering. Considerations in offering a robot-generated Web index as a successor to a manually indexed quality-controlled subject gateway are also discussed
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  18. Shafer, K.E.: Evaluating Scorpion Results (2001) 0.00 
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  19. Shen, D.; Chen, Z.; Yang, Q.; Zeng, H.J.; Zhang, B.; Lu, Y.; Ma, W.Y.: Web page classification through summarization (2004) 0.00 
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  20. Sebastiani, F.: Classification of text, automatic (2006) 0.00 
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    Abstract
    Automatic text classification (ATC) is a discipline at the crossroads of information retrieval (IR), machine learning (ML), and computational linguistics (CL), and consists in the realization of text classifiers, i.e. software systems capable of assigning texts to one or more categories, or classes, from a predefined set. Applications range from the automated indexing of scientific articles, to e-mail routing, spam filtering, authorship attribution, and automated survey coding. This article will focus on the ML approach to ATC, whereby a software system (called the learner) automatically builds a classifier for the categories of interest by generalizing from a "training" set of pre-classified texts.
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