Search (9 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  • × theme_ss:"Computerlinguistik"
  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. 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
  3. Zhang, X: Rough set theory based automatic text categorization (2005) 0.00 
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  4. Ruiz, M.E.; Srinivasan, P.: Combining machine learning and hierarchical indexing structures for text categorization (2001) 0.00 
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  5. Sebastiani, F.: Machine learning in automated text categorization (2002) 0.00 
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  6. Sebastiani, F.: ¬A tutorial an automated text categorisation (1999) 0.00 
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  7. Duwairi, R.M.: Machine learning for Arabic text categorization (2006) 0.00 
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  8. Ko, Y.: ¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities (2015) 0.00 
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  9. Peng, F.; Huang, X.: Machine learning for Asian language text classification (2007) 0.00 
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