Search (7 results, page 1 of 1)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Computerlinguistik"
  1. Ghazzawi, N.; Robichaud, B.; Drouin, P.; Sadat, F.: Automatic extraction of specialized verbal units (2018) 0.01
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    Abstract
    This paper presents a methodology for the automatic extraction of specialized Arabic, English and French verbs of the field of computing. Since nominal terms are predominant in terminology, our interest is to explore to what extent verbs can also be part of a terminological analysis. Hence, our objective is to verify how an existing extraction tool will perform when it comes to specialized verbs in a given specialized domain. Furthermore, we want to investigate any particularities that a language can represent regarding verbal terms from the automatic extraction perspective. Our choice to operate on three different languages reflects our desire to see whether the chosen tool can perform better on one language compared to the others. Moreover, given that Arabic is a morphologically rich and complex language, we consider investigating the results yielded by the extraction tool. The extractor used for our experiment is TermoStat (Drouin 2003). So far, our results show that the extraction of verbs of computing represents certain differences in terms of quality and particularities of these units in this specialized domain between the languages under question.
  2. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01
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    Date
    22. 3.2015 9:30:24
  3. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.01
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    Date
    10. 1.2013 19:22:47
  4. Lawrie, D.; Mayfield, J.; McNamee, P.; Oard, P.W.: Cross-language person-entity linking from 20 languages (2015) 0.01
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    Abstract
    The goal of entity linking is to associate references to an entity that is found in unstructured natural language content to an authoritative inventory of known entities. This article describes the construction of 6 test collections for cross-language person-entity linking that together span 22 languages. Fully automated components were used together with 2 crowdsourced validation stages to affordably generate ground-truth annotations with an accuracy comparable to that of a completely manual process. The resulting test collections each contain between 642 (Arabic) and 2,361 (Romanian) person references in non-English texts for which the correct resolution in English Wikipedia is known, plus a similar number of references for which no correct resolution into English Wikipedia is believed to exist. Fully automated cross-language person-name linking experiments with 20 non-English languages yielded a resolution accuracy of between 0.84 (Serbian) and 0.98 (Romanian), which compares favorably with previously reported cross-language entity linking results for Spanish.
  5. Fóris, A.: Network theory and terminology (2013) 0.01
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    Date
    2. 9.2014 21:22:48
  6. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.01
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    Date
    22. 1.2018 11:32:44
  7. Deventer, J.P. van; Kruger, C.J.; Johnson, R.D.: Delineating knowledge management through lexical analysis : a retrospective (2015) 0.01
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    Date
    20. 1.2015 18:30:22