Search (112 results, page 1 of 6)

  • × theme_ss:"Computerlinguistik"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.15 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.10 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  3. Huo, W.: Automatic multi-word term extraction and its application to Web-page summarization (2012) 0.08 
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    Content
    A Thesis presented to The University of Guelph In partial fulfilment of requirements for the degree of Master of Science in Computer Science. Vgl. Unter: http://www.inf.ufrgs.br%2F~ceramisch%2Fdownload_files%2Fpublications%2F2009%2Fp01.pdf.
    Date
    10. 1.2013 19:22:47
  4. Çelebi, A.; Özgür, A.: Segmenting hashtags and analyzing their grammatical structure (2018) 0.02 
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    Abstract
    Originated as a label to mark specific tweets, hashtags are increasingly used to convey messages that people like to see in the trending hashtags list. Complex noun phrases and even sentences can be turned into a hashtag. Breaking hashtags into their words is a challenging task due to the irregular and compact nature of the language used in Twitter. In this study, we investigate feature-based machine learning and language model (LM)-based approaches for hashtag segmentation. Our results show that LM alone is not successful at segmenting nontrivial hashtags. However, when the N-best LM-based segmentations are incorporated as features into the feature-based approach, along with context-based features proposed in this study, state-of-the-art results in hashtag segmentation are achieved. In addition, we provide an analysis of over two million distinct hashtags, autosegmented by using our best configuration. The analysis reveals that half of all 60 million hashtag occurrences contain multiple words and 80% of sentiment is trapped inside multiword hashtags, justifying the need for hashtag segmentation. Furthermore, we analyze the grammatical structure of hashtags by parsing them and observe that 77% of the hashtags are noun-based, whereas 11.9% are verb-based.
  5. Melby, A.: Some notes on 'The proper place of men and machines in language translation' (1997) 0.01 
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    Date
    31. 7.1996 9:22:19
    Source
    Machine translation. 12(1997) nos.1/2, S.29-34
  6. 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
  7. Kuhlen, R.: Morphologische Relationen durch Reduktionsalgorithmen (1974) 0.01 
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    Date
    29. 1.2011 14:56:29
  8. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.01 
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    Date
    29. 3.2023 13:23:26
    29. 3.2023 13:29:19
  9. Wettler, M.; Rapp, R.; Ferber, R.: Freie Assoziationen und Kontiguitäten von Wörtern in Texten (1993) 0.01 
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    Date
    4.11.1998 14:30:29
  10. Warner, A.J.: Natural language processing (1987) 0.01 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  11. Hahn, U.; Reimer, U.: Informationslinguistische Konzepte der Volltextverarbeitung in TOPIC (1983) 0.00 
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    Source
    Deutscher Dokumentartag 1982, Lübeck-Travemünde, 29.-30.9.1982: Fachinformation im Zeitalter der Informationsindustrie. Bearb.: H. Strohl-Goebel
  12. Proszeky, G.: Language technology tools in the translator's practice (1999) 0.00 
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    Date
    30. 3.2002 18:29:40
  13. McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.00 
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    Source
    Computational linguistics. 22(1996) no.2, S.217-248
  14. Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.00 
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    Date
    8.10.2000 11:52:22
  15. Somers, H.: Example-based machine translation : Review article (1999) 0.00 
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    Date
    31. 7.1996 9:22:19
  16. New tools for human translators (1997) 0.00 
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    Date
    31. 7.1996 9:22:19
  17. Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.00 
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    Date
    28. 2.1999 10:48:22
  18. ¬Der Student aus dem Computer (2023) 0.00 
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    Date
    27. 1.2023 16:22:55
  19. Zue, V.: Dein Computer spricht mir dir (1999) 0.00 
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    Date
    31.12.1996 19:29:41
  20. Mustafa El Hadi, W.: Evaluating human language technology : general applications to information access and management (2002) 0.00 
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    Source
    Knowledge organization. 29(2002) nos.3/4, S.124-134

Authors

Years

Languages

  • e 77
  • d 31
  • ru 2
  • More… Less…

Types

  • a 93
  • el 12
  • m 9
  • s 4
  • x 3
  • p 2
  • d 1
  • More… Less…

Themes

Subjects

Classifications