Search (20 results, page 1 of 1)

  • × theme_ss:"Computerlinguistik"
  • × year_i:[2010 TO 2020}
  1. Fóris, A.: Network theory and terminology (2013) 0.04 
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    Date
    2. 9.2014 19:19:40
    2. 9.2014 21:22:48
    Source
    Knowledge organization. 40(2013) no.6, S.424-429
  2. Rötzer, F.: Kann KI mit KI generierte Texte erkennen? (2019) 0.01 
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    Abstract
    OpenAI hat einen Algorithmus zur Textgenerierung angeblich nicht vollständig veröffentlicht, weil er so gut sei und Missbrauch und Täuschung ermöglicht. Das u.a. von Elon Musk und Peter Thiel gegründete KI-Unternehmen OpenAI hatte im Februar erklärt, man habe den angeblich am weitesten fortgeschrittenen Algorithmus zur Sprachverarbeitung entwickelt. Der Algorithmus wurde lediglich anhand von 40 Gigabyte an Texten oder an 8 Millionen Webseiten trainiert, das nächste Wort in einem vorgegebenen Textausschnitt vorherzusagen. Damit könne man zusammenhängende, sinnvolle Texte erzeugen, die vielen Anforderungen genügen, zudem könne damit rudimentär Leseverständnis, Antworten auf Fragen, Zusammenfassungen und Übersetzungen erzeugt werden, ohne dies trainiert zu haben.
  3. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
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    Date
    22. 3.2015 9:30:24
  4. Altmann, E.G.; Cristadoro, G.; Esposti, M.D.: On the origin of long-range correlations in texts (2012) 0.01 
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    Date
    24. 7.2012 11:40:06
  5. Rosemblat, G.; Resnick, M.P.; Auston, I.; Shin, D.; Sneiderman, C.; Fizsman, M.; Rindflesch, T.C.: Extending SemRep to the public health domain (2013) 0.01 
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    Date
    22.10.2013 19:54:40
  6. Anguiano Peña, G.; Naumis Peña, C.: Method for selecting specialized terms from a general language corpus (2015) 0.01 
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    2. 9.2014 19:19:40
  7. Li, N.; Sun, J.: Improving Chinese term association from the linguistic perspective (2017) 0.01 
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    2. 9.2014 19:19:40
  8. Lu, K.; Cai, X.; Ajiferuke, I.; Wolfram, D.: Vocabulary size and its effect on topic representation (2017) 0.01 
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    Abstract
    This study investigates how computational overhead for topic model training may be reduced by selectively removing terms from the vocabulary of text corpora being modeled. We compare the impact of removing singly occurring terms, the top 0.5%, 1% and 5% most frequently occurring terms and both top 0.5% most frequent and singly occurring terms, along with changes in the number of topics modeled (10, 20, 30, 40, 50, 100) using three datasets. Four outcome measures are compared. The removal of singly occurring terms has little impact on outcomes for all of the measures tested. Document discriminative capacity, as measured by the document space density, is reduced by the removal of frequently occurring terms, but increases with higher numbers of topics. Vocabulary size does not greatly influence entropy, but entropy is affected by the number of topics. Finally, topic similarity, as measured by pairwise topic similarity and Jensen-Shannon divergence, decreases with the removal of frequent terms. The findings have implications for information science research in information retrieval and informetrics that makes use of topic modeling.
  9. 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
  10. 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.
  11. Gencosman, B.C.; Ozmutlu, H.C.; Ozmutlu, S.: Character n-gram application for automatic new topic identification (2014) 0.01 
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    Date
    25. 1.2016 18:44:40
  12. Lian, T.; Yu, C.; Wang, W.; Yuan, Q.; Hou, Z.: Doctoral dissertations on tourism in China : a co-word analysis (2016) 0.01 
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    2. 9.2014 19:19:40
  13. Collovini de Abreu, S.; Vieira, R.: RelP: Portuguese open relation extraction (2017) 0.01 
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    2. 9.2014 19:19:40
  14. Chen, L.; Fang, H.: ¬An automatic method for ex-tracting innovative ideas based on the Scopus® database (2019) 0.01 
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    2. 9.2014 19:19:40
  15. Vlachidis, A.; Binding, C.; Tudhope, D.; May, K.: Excavating grey literature : a case study on the rich indexing of archaeological documents via natural language-processing techniques and knowledge-based resources (2010) 0.01 
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    Date
    29. 8.2010 12:03:40
  16. Bowker, L.; Ciro, J.B.: Machine translation and global research : towards improved machine translation literacy in the scholarly community (2019) 0.01 
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    Date
    12. 9.2020 20:40:49
  17. Nagy T., I.: Detecting multiword expressions and named entities in natural language texts (2014) 0.01 
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    30.10.2014 18:40:33
  18. 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
  19. RWI/PH: Auf der Suche nach dem entscheidenden Wort : die Häufung bestimmter Wörter innerhalb eines Textes macht diese zu Schlüsselwörtern (2012) 0.00 
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    24. 7.2012 11:40:06
  20. Deventer, J.P. van; Kruger, C.J.; Johnson, R.D.: Delineating knowledge management through lexical analysis : a retrospective (2015) 0.00 
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    Date
    20. 1.2015 18:30:22

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