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  • × theme_ss:"Computerlinguistik"
  1. Warner, A.J.: Natural language processing (1987) 0.00 
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    Source
    Annual review of information science and technology. 22(1987), S.79-108
  2. 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) 0.00 
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    Abstract
    Detecting negative and speculative information is essential in most biomedical text-mining tasks where these language forms are used to express impressions, hypotheses, or explanations of experimental results. Our research is focused on developing a system based on machine-learning techniques that identifies negation and speculation signals and their scope in clinical texts. The proposed system works in two consecutive phases: first, a classifier decides whether each token in a sentence is a negation/speculation signal or not. Then another classifier determines, at sentence level, the tokens which are affected by the signals previously identified. The system was trained and evaluated on the clinical texts of the BioScope corpus, a freely available resource consisting of medical and biological texts: full-length articles, scientific abstracts, and clinical reports. The results obtained by our system were compared with those of two different systems, one based on regular expressions and the other based on machine learning. Our system's results outperformed the results obtained by these two systems. In the signal detection task, the F-score value was 97.3% in negation and 94.9% in speculation. In the scope-finding task, a token was correctly classified if it had been properly identified as being inside or outside the scope of all the negation signals present in the sentence. Our proposal showed an F score of 93.2% in negation and 80.9% in speculation. Additionally, the percentage of correct scopes (those with all their tokens correctly classified) was evaluated obtaining F scores of 90.9% in negation and 71.9% in speculation.
  3. 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
  4. 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
  5. Somers, H.: Example-based machine translation : Review article (1999) 0.00 
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    Date
    31. 7.1996 9:22:19
  6. New tools for human translators (1997) 0.00 
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    Date
    31. 7.1996 9:22:19
  7. 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
  8. ¬Der Student aus dem Computer (2023) 0.00 
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    Date
    27. 1.2023 16:22:55
  9. Polity, Y.: Vers une ergonomie linguistique (1994) 0.00 
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    Language
    f
  10. Ahmad, F.; Yusoff, M.; Sembok, T.M.T.: Experiments with a stemming algorithm for Malay words (1996) 0.00 
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  11. Vazov, N.: Identification des differentes structures temporelles dans des textes et leur rôles dans le raisonnement temporel (1999) 0.00 
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    Language
    f
  12. Ferret, O.; Grau, B.; Masson, N.: Utilisation d'un réseau de cooccurences lexikales pour a méliorer une analyse thématique fondée sur la distribution des mots (1999) 0.00 
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    Language
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  13. Fattah, M. Abdel; Ren, F.: English-Arabic proper-noun transliteration-pairs creation (2008) 0.00 
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    Abstract
    Proper nouns may be considered the most important query words in information retrieval. If the two languages use the same alphabet, the same proper nouns can be found in either language. However, if the two languages use different alphabets, the names must be transliterated. Short vowels are not usually marked on Arabic words in almost all Arabic documents (except very important documents like the Muslim and Christian holy books). Moreover, most Arabic words have a syllable consisting of a consonant-vowel combination (CV), which means that most Arabic words contain a short or long vowel between two successive consonant letters. That makes it difficult to create English-Arabic transliteration pairs, since some English letters may not be matched with any romanized Arabic letter. In the present study, we present different approaches for extraction of transliteration proper-noun pairs from parallel corpora based on different similarity measures between the English and romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the new approaches presented using two different English-Arabic parallel corpora. Most of our results outperform previously published results in terms of precision, recall, and F-Measure.
  14. Gomez, F.: Learning word syntactic subcategorizations interactively (1995) 0.00 
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  15. Szpakowicz, S.; Bond, F.; Nakov, P.; Kim, S.N.: On the semantics of noun compounds (2013) 0.00 
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  16. Colace, F.; Santo, M. De; Greco, L.; Napoletano, P.: Weighted word pairs for query expansion (2015) 0.00 
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  17. Rötzer, F.: Kann KI mit KI generierte Texte erkennen? (2019) 0.00 
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  18. Sünkler, S.; Kerkmann, F.; Schultheiß, S.: Ok Google . the end of search as we know it : sprachgesteuerte Websuche im Test (2018) 0.00 
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  19. 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
  20. 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

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