Search (21 results, page 1 of 2)

  • × year_i:[2010 TO 2020}
  • × theme_ss:"Computerlinguistik"
  1. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01
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    Date
    22. 3.2015 9:30:24
  2. 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
  3. Ludwig, B.; Reischer, J.: Informationslinguistik in Regensburg (2012) 0.01
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    Date
    7. 1.2013 20:59:44
  4. Budin, G.: Zum Entwicklungsstand der Terminologiewissenschaft (2019) 0.01
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    Pages
    S.7-19
  5. 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
  6. 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.
  7. Panicheva, P.; Cardiff, J.; Rosso, P.: Identifying subjective statements in news titles using a personal sense annotation framework (2013) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1411-1422
  8. Vechtomova, O.: ¬A method for automatic extraction of multiword units representing business aspects from user reviews (2014) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.7, S.1463-1477
  9. Schöneberg, U.; Sperber, W.: POS tagging and its applications for mathematics (2014) 0.01
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    Source
    Intelligent Computer Mathematics - International Conference, CICM 2014, Coimbra, Portugal, July 7-11, 2014. Proceedings
  10. Xinglin, L.: Automatic summarization method based on compound word recognition (2015) 0.01
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    Date
    7. 5.2015 17:32:42
  11. Fóris, A.: Network theory and terminology (2013) 0.01
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    Date
    2. 9.2014 21:22:48
  12. Cruz Díaz, N.P.; Maña López, M.J.; Mata Vázquez, J.; Pachón Álvarez, V.: ¬A machine-learning approach to negation and speculation detection in clinical texts (2012) 0.01
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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.7, S.1398-1410
  13. Multi-source, multilingual information extraction and summarization (2013) 0.01
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    Footnote
    Rez. in: JASIST 64(2013) no.7, S.1519-1521 (José L. Vicedo, David Tomás)
  14. Hoenkamp, E.; Bruza, P.: How everyday language can and will boost effective information retrieval (2015) 0.01
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    Date
    7. 7.2015 21:41:32
  15. Gill, A.J.; Hinrichs-Krapels, S.; Blanke, T.; Grant, J.; Hedges, M.; Tanner, S.: Insight workflow : systematically combining human and computational methods to explore textual data (2017) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.7, S.1671-1686
  16. Muneer, I.; Sharjeel, M.; Iqbal, M.; Adeel Nawab, R.M.; Rayson, P.: CLEU - A Cross-language english-urdu corpus and benchmark for text reuse experiments (2019) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.7, S.729-741
  17. 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
  18. Karlova-Bourbonus, N.: Automatic detection of contradictions in texts (2018) 0.01
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    Abstract
    Natural language contradictions are of complex nature. As will be shown in Chapter 5, the realization of contradictions is not limited to the examples such as Socrates is a man and Socrates is not a man (under the condition that Socrates refers to the same object in the real world), which is discussed by Aristotle (Section 3.1.1). Empirical evidence (see Chapter 5 for more details) shows that only a few contradictions occurring in the real life are of that explicit (prototypical) kind. Rather, con-tradictions make use of a variety of natural language devices such as, e.g., paraphrasing, synonyms and antonyms, passive and active voice, diversity of negation expression, and figurative linguistic means such as idioms, irony, and metaphors. Additionally, the most so-phisticated kind of contradictions, the so-called implicit contradictions, can be found only when applying world knowledge and after conducting a sequence of logical operations such as e.g. in: (1.1) The first prize was given to the experienced grandmaster L. Stein who, in total, col-lected ten points (7 wins and 3 draws). Those familiar with the chess rules know that a chess player gets one point for winning and zero points for losing the game. In case of a draw, each player gets a half point. Built on this idea and by conducting some simple mathematical operations, we can infer that in the case of 7 wins and 3 draws (the second part of the sentence), a player can only collect 8.5 points and not 10 points. Hence, we observe that there is a contradiction between the first and the second parts of the sentence.
  19. Ramisch, C.: Multiword expressions acquisition : a generic and open framework (2015) 0.01
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    Content
    1.Introduction.- Part I.Multiword Expressions: a Tough Nut to Crack.- 2.Definitions and Characteristics.- 3 State of the Art in MWE Processing.- Part II.MWE Acquisition.- 4.Evaluation of MWE Acquisition.- 5.A New Framework for MWE Acquisition.- Part III Applications.- 6.Application 1: Lexicography.- 7.Application 2: Machine Translation.- 8.Conclusions.- Appendixes.- A.Extended List of Translation Examples.- B.Resources Used in the Experiments.- C.The mwetoolkit: Documentation.- D.Tagsets for POS and syntax.- E.Detailed Lexicon Descriptions.
  20. Computerlinguistik und Sprachtechnologie : Eine Einführung (2010) 0.01
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    Isbn
    978-3-8274-2023-7