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Sökning: WFRF:(Grobelnik Marko)

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  • Rehm, Georg, et al. (författare)
  • The strategic impact of META-NET on the regional, national and international level
  • 2016
  • Ingår i: Language resources and evaluation. - : Springer Science and Business Media LLC. - 1574-020X .- 1572-8412 .- 1574-0218. ; 50:2, s. 351-374
  • Tidskriftsartikel (refereegranskat)abstract
    • This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative’s work throughout Europe in order to boost progress and innovation in our field.
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  • Golub, Koraljka, et al. (författare)
  • Comparing and combining two approaches to automated subject classification of text
  • 2006
  • Ingår i: Research and advanced technology for digital libraries. - Berlin, Heidelberg : Springer. - 9783540446361 - 9783540446385 ; 4172, s. 467-470
  • Konferensbidrag (refereegranskat)abstract
    • A machine-learning and a string-matching approach to automated subject classification of text were compared, as to their performance, advantages and downsides. The former approach was based on an SVM algorithm, while the latter comprised string-matching between a controlled vocabulary and words in the text to be classified. Data collection consisted of a subset from Compendex, classified into six different classes. It was shown that SVM on average outperforms the string-matching approach: our hypothesis that SVM yields better recall and string-matching better precision was confirmed only on one of the classes. The two approaches being complementary, we investigated different combinations of the two based on combining their vocabularies. The results have shown that the original approaches, i.e. machine-learning approach without using background knowledge from the controlled vocabulary, and string-matching approach based on controlled vocabulary, outperform approaches in which combinations of automatically and manually obtained terms were used. Reasons for these results need further investigation, including a larger data collection and combining the two using predictions.
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