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  • × language_ss:"e"
  • × 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. 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
  8. Proszeky, G.: Language technology tools in the translator's practice (1999) 0.00 
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    Date
    30. 3.2002 18:29:40
  9. 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
  10. 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
  11. Somers, H.: Example-based machine translation : Review article (1999) 0.00 
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    Date
    31. 7.1996 9:22:19
  12. New tools for human translators (1997) 0.00 
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    Date
    31. 7.1996 9:22:19
  13. 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
  14. 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
  15. Byrne, C.C.; McCracken, S.A.: ¬An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval (1999) 0.00 
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    Date
    15. 3.2000 10:22:37
  16. Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.00 
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    Date
    1. 3.2013 14:56:22
  17. Griffiths, T.L.; Steyvers, M.: ¬A probabilistic approach to semantic representation (2002) 0.00 
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    Date
    29. 6.2015 14:55:01
    29. 6.2015 16:09:05
  18. Czejdo. B.D.; Tucci, R.P.: ¬A dataflow graphical language for database applications (1994) 0.00 
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    Date
    20.10.2000 13:29:46
  19. Whitelock, P.; Kilby, K.: Linguistic and computational techniques in machine translation system design : 2nd ed (1995) 0.00 
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    Date
    29. 3.1996 18:28:09
  20. Rau, L.F.: Conceptual information extraction and retrieval from natural language input (198) 0.00 
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    Date
    16. 8.1998 13:29:20

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