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  • × theme_ss:"Computerlinguistik"
  1. Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.02 
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    Date
    31. 7.1996 9:22:19
  2. Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.02 
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    Source
    c't. 2000, H.22, S.220-229
  3. Lezius, W.; Rapp, R.; Wettler, M.: ¬A morphology-system and part-of-speech tagger for German (1996) 0.02 
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    Date
    22. 3.2015 9:37:18
  4. Spitkovsky, V.; Norvig, P.: From words to concepts and back : dictionaries for linking text, entities and ideas (2012) 0.02 
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    Abstract
    Human language is both rich and ambiguous. When we hear or read words, we resolve meanings to mental representations, for example recognizing and linking names to the intended persons, locations or organizations. Bridging words and meaning - from turning search queries into relevant results to suggesting targeted keywords for advertisers - is also Google's core competency, and important for many other tasks in information retrieval and natural language processing. We are happy to release a resource, spanning 7,560,141 concepts and 175,100,788 unique text strings, that we hope will help everyone working in these areas. How do we represent concepts? Our approach piggybacks on the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases. We consider each individual Wikipedia article as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link) that point to each Wikipedia page, thus drawing on the vast link structure of the web. For every English article we harvested the strings associated with its incoming hyperlinks from the rest of Wikipedia, the greater web, and also anchors of parallel, non-English Wikipedia pages. Our dictionaries are cross-lingual, and any concept deemed too fine can be broadened to a desired level of generality using Wikipedia's groupings of articles into hierarchical categories. The data set contains triples, each consisting of (i) text, a short, raw natural language string; (ii) url, a related concept, represented by an English Wikipedia article's canonical location; and (iii) count, an integer indicating the number of times text has been observed connected with the concept's url. Our database thus includes weights that measure degrees of association. For example, the top two entries for football indicate that it is an ambiguous term, which is almost twice as likely to refer to what we in the US call soccer. Vgl. auch: Spitkovsky, V.I., A.X. Chang: A cross-lingual dictionary for english Wikipedia concepts. In: http://nlp.stanford.edu/pubs/crosswikis.pdf.
  5. Wanner, L.: Lexical choice in text generation and machine translation (1996) 0.01 
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    Date
    31. 7.1996 9:22:19
  6. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  7. Basili, R.; Pazienza, M.T.; Velardi, P.: ¬An empirical symbolic approach to natural language processing (1996) 0.01 
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    Date
    6. 3.1997 16:22:15
  8. Haas, S.W.: Natural language processing : toward large-scale, robust systems (1996) 0.01 
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    Abstract
    State of the art review of natural language processing updating an earlier review published in ARIST 22(1987). Discusses important developments that have allowed for significant advances in the field of natural language processing: materials and resources; knowledge based systems and statistical approaches; and a strong emphasis on evaluation. Reviews some natural language processing applications and common problems still awaiting solution. Considers closely related applications such as language generation and th egeneration phase of machine translation which face the same problems as natural language processing. Covers natural language methodologies for information retrieval only briefly
  9. Way, E.C.: Knowledge representation and metaphor (oder: meaning) (1994) 0.01 
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    Footnote
    Bereits 1991 bei Kluwer publiziert // Rez. in: Knowledge organization 22(1995) no.1, S.48-49 (O. Sechser)
  10. Ruge, G.: Sprache und Computer : Wortbedeutung und Termassoziation. Methoden zur automatischen semantischen Klassifikation (1995) 0.01 
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    Footnote
    Rez. in: Knowledge organization 22(1995) no.3/4, S.182-184 (M.T. Rolland)
  11. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
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    Date
    22. 3.2015 9:30:24
  12. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01 
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    Date
    2. 3.2020 19:04:22
  13. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.01 
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    Date
    29.12.2022 18:22:55
  14. Rieger, F.: Lügende Computer (2023) 0.01 
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    Date
    16. 3.2023 19:22:55
  15. Doszkocs, T.E.; Zamora, A.: Dictionary services and spelling aids for Web searching (2004) 0.01 
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    Date
    14. 8.2004 17:22:56
    Source
    Online. 28(2004) no.3, S.22-29
  16. Schwarz, C.: THESYS: Thesaurus Syntax System : a fully automatic thesaurus building aid (1988) 0.01 
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    Date
    6. 1.1999 10:22:07
  17. Kay, M.: ¬The proper place of men and machines in language translation (1997) 0.01 
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    Date
    31. 7.1996 9:22:19
  18. Liddy, E.D.: Natural language processing for information retrieval and knowledge discovery (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  19. Godby, J.: WordSmith research project bridges gap between tokens and indexes (1998) 0.01 
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    Source
    OCLC newsletter. 1998, no.234, Jul/Aug, S.22-24
  20. Hammwöhner, R.: TransRouter revisited : Decision support in the routing of translation projects (2000) 0.01 
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    Date
    10.12.2000 18:22:35

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