Search (21 results, page 1 of 2)

  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. 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.
  2. Engerer, V.: Informationswissenschaft und Linguistik. : kurze Geschichte eines fruchtbaren interdisziplinäaren Verhäaltnisses in drei Akten (2012) 0.01 
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    SDV - Sprache und Datenverarbeitung. International journal for language data processing. 36(2012) H.2, S.71-91 [= E-Books - Fakten, Perspektiven und Szenarien] 36/2 (2012), S. 71-91
  3. Fóris, A.: Network theory and terminology (2013) 0.01 
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    Date
    2. 9.2014 21:22:48
  4. Korman, D.Z.; Mack, E.; Jett, J.; Renear, A.H.: Defining textual entailment (2018) 0.01 
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    Abstract
    Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H?=?df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.
  5. Heid, U.: Computerlinguistik zwischen Informationswissenschaft und multilingualer Kommunikation (2010) 0.01 
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    Source
    Information - Wissenschaft und Praxis. 61(2010) H.6/7, S.361-366
  6. Ludwig, B.; Reischer, J.: Informationslinguistik in Regensburg (2012) 0.01 
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    Information - Wissenschaft und Praxis. 63(2012) H.5, S.292-296
  7. Baierer, K.; Zumstein, P.: Verbesserung der OCR in digitalen Sammlungen von Bibliotheken (2016) 0.01 
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    027.7 Zeitschrift für Bibliothekskultur. 4(2016), H.2
  8. 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
  9. Keselman, A.; Rosemblat, G.; Kilicoglu, H.; Fiszman, M.; Jin, H.; Shin, D.; Rindflesch, T.C.: Adapting semantic natural language processing technology to address information overload in influenza epidemic management (2010) 0.01 
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  10. Agarwal, B.; Ramampiaro, H.; Langseth, H.; Ruocco, M.: ¬A deep network model for paraphrase detection in short text messages (2018) 0.01 
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  11. Wu, H.; He, J.; Pei, Y.: Scientific impact at the topic level : a case study in computational linguistics (2010) 0.01 
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  12. Strube, M.: Kreativ durch Analogien (2011) 0.01 
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    Source
    Spektrum der Wissenschaft. 2011, H.12, S.30-33
  13. Radev, D.R.; Joseph, M.T.; Gibson, B.; Muthukrishnan, P.: ¬A bibliometric and network analysis of the field of computational linguistics (2016) 0.01 
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    Abstract
    The ACL Anthology is a large collection of research papers in computational linguistics. Citation data were obtained using text extraction from a collection of PDF files with significant manual postprocessing performed to clean up the results. Manual annotation of the references was then performed to complete the citation network. We analyzed the networks of paper citations, author citations, and author collaborations in an attempt to identify the most central papers and authors. The analysis includes general network statistics, PageRank, metrics across publication years and venues, the impact factor and h-index, as well as other measures.
  14. Sünkler, S.; Kerkmann, F.; Schultheiß, S.: Ok Google . the end of search as we know it : sprachgesteuerte Websuche im Test (2018) 0.01 
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    B.I.T.online. 21(2018) H.1, S.25-32
  15. Endres-Niggemeyer, B.: Thinkie: Lautes Denken mit Spracherkennung (mobil) (2013) 0.00 
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    Information - Wissenschaft und Praxis. 64(2013) H.6, S.311-322
  16. Geißler, S.: Maschinelles Lernen und NLP : Reif für die industrielle Anwendung! (2019) 0.00 
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    Information - Wissenschaft und Praxis. 70(2019) H.2/3, S.134-140
  17. Hmeidi, I.I.; Al-Shalabi, R.F.; Al-Taani, A.T.; Najadat, H.; Al-Hazaimeh, S.A.: ¬A novel approach to the extraction of roots from Arabic words using bigrams (2010) 0.00 
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  18. Franke-Maier, M.: Computerlinguistik und Bibliotheken : Editorial (2016) 0.00 
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    027.7 Zeitschrift für Bibliothekskultur. 4(2016), H.2
  19. Järvelin, A.; Keskustalo, H.; Sormunen, E.; Saastamoinen, M.; Kettunen, K.: Information retrieval from historical newspaper collections in highly inflectional languages : a query expansion approach (2016) 0.00 
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  20. Chen, L.; Fang, H.: ¬An automatic method for ex-tracting innovative ideas based on the Scopus® database (2019) 0.00 
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Authors

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  • e 10

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