Search (100 results, page 1 of 5)

  • × theme_ss:"Computerlinguistik"
  1. Semantik, Lexikographie und Computeranwendungen : Workshop ... (Bonn) : 1995.01.27-28 (1996) 0.06 
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    Classification
    Spr F 510
    Spr F 87 / Lexikographie
    Date
    14. 4.2007 10:04:22
    SBB
    Spr F 510
    Spr F 87 / Lexikographie
  2. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.06 
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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
  3. Rieger, F.: Lügende Computer (2023) 0.06 
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    Date
    16. 3.2023 19:22:55
  4. Schneider, J.W.; Borlund, P.: ¬A bibliometric-based semiautomatic approach to identification of candidate thesaurus terms : parsing and filtering of noun phrases from citation contexts (2005) 0.05 
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    Date
    8. 3.2007 19:55:22
    Source
    Context: nature, impact and role. 5th International Conference an Conceptions of Library and Information Sciences, CoLIS 2005 Glasgow, UK, June 2005. Ed. by F. Crestani u. I. Ruthven
  5. Vichot, F.; Wolinksi, F.; Tomeh, J.; Guennou, S.; Dillet, B.; Aydjian, S.: High precision hypertext navigation based on NLP automation extractions (1997) 0.04 
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  6. Latzer, F.-M.: Yo Computa! (1997) 0.04 
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  7. Blanchon, E.: Terminology software : pt.1.2 (1995) 0.04 
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    Language
    f
  8. Gonzalo, J.; Verdejo, F.; Peters, C.; Calzolari, N.: Applying EuroWordNet to cross-language text retrieval (1998) 0.04 
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  9. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.04 
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    Date
    23.11.2023 19:07:22
  10. Hull, D.; Ait-Mokhtar, S.; Chuat, M.; Eisele, A.; Gaussier, E.; Grefenstette, G.; Isabelle, P.; Samulesson, C.; Segand, F.: Language technologies and patent search and classification (2001) 0.03 
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  11. Rodriguez, H.; Climent, S.; Vossen, P.; Bloksma, L.; Peters, W.; Alonge, A.; Bertagna, F.; Roventini, A.: ¬The top-down strategy for building EuroWordNet : vocabulary coverage, base concept and top ontology (1998) 0.03 
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  12. Rötzer, F.: KI-Programm besser als Menschen im Verständnis natürlicher Sprache (2018) 0.03 
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    Date
    22. 1.2018 11:32:44
  13. Wenzel, F.: Semantische Eingrenzung im Freitext-Retrieval auf der Basis morphologischer Segmentierungen (1980) 0.03 
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  14. Chibout, K.; Vilnat, A.: Primitive sémantiques, classification des verbes et polysémie (1999) 0.03 
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  15. Rosemblat, G.; Tse, T.; Gemoets, D.: Adapting a monolingual consumer health system for Spanish cross-language information retrieval (2004) 0.03 
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    Abstract
    This preliminary study applies a bilingual term list (BTL) approach to cross-language information retrieval (CLIR) in the consumer health domain and compares it to a machine translation (MT) approach. We compiled a Spanish-English BTL of 34,980 medical and general terms. We collected a training set of 466 general health queries from MedlinePlus en espaiiol and 488 domainspecific queries from ClinicalTrials.gov translated into Spanish. We submitted the training set queries in English against a test bed of 7,170 ClinicalTrials.gov English documents, and compared MT and BTL against this English monolingual standard. The BTL approach was less effective (F = 0.420) than the MT approach (F = 0.578). A failure analysis of the results led to substitution of BTL dictionary sources and the addition of rudimentary normalisation of plural forms. These changes improved the CLIR effectiveness of the same training set queries (F = 0.474), and yielded comparable results for a test set of new 954 queries (F= 0.484). These results will shape our efforts to support Spanishspeakers' needs for consumer health information currently only available in English.
  16. Liu, S.; Liu, F.; Yu, C.; Meng, W.: ¬An effective approach to document retrieval via utilizing WordNet and recognizing phrases (2004) 0.03 
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  17. Rettinger, A.; Schumilin, A.; Thoma, S.; Ell, B.: Learning a cross-lingual semantic representation of relations expressed in text (2015) 0.03 
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    Source
    The Semantic Web: latest advances and new domains. 12th European Semantic Web Conference, ESWC 2015 Portoroz, Slovenia, May 31 -- June 4, 2015. Proceedings. Eds.: F. Gandon u.a
  18. Kocijan, K.: Visualizing natural language resources (2015) 0.03 
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    Re:inventing information science in the networked society: Proceedings of the 14th International Symposium on Information Science, Zadar/Croatia, 19th-21st May 2015. Eds.: F. Pehar, C. Schloegl u. C. Wolff
  19. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.02 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  20. 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) 0.02 
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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.

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