Search (2207 results, page 1 of 111)

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  1. Frants, V.I.; Shapiro, J.; Voiskunskii, V.G.: Optimal search available to an individual user (1996) 0.16 
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    Source
    Journal of information science. 22(1996) no.3, S.181-191
  2. Walch, V.I.: Standards for archival description : a handbook (199?) 0.12 
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  3. Walsh, V.I.: Standards of archival description : a handbook. With contributions by M. Matters (1994) 0.12 
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  4. Ackermann, E.: Piaget's constructivism, Papert's constructionism : what's the difference? (2001) 0.11 
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    Content
    Vgl.: https://www.semanticscholar.org/paper/Piaget-%E2%80%99-s-Constructivism-%2C-Papert-%E2%80%99-s-%3A-What-%E2%80%99-s-Ackermann/89cbcc1e740a4591443ff4765a6ae8df0fdf5554. Darunter weitere Hinweise auf verwandte Beiträge. Auch unter: Learning Group Publication 5(2001) no.3, S.438.
  5. Frants, V.I.; Shapiro, J.: Control and feedback in a documentary information retrieval system (1991) 0.08 
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  6. Frants, V.I.; Kamenoff, N.I.; Shapiro, J.: ¬One approach to classification of users and automatic clustering of documents (1993) 0.08 
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  7. Frants, V.I.: Development of IR systems : new direction (1996) 0.08 
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  8. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.07 
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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
  9. Frants, V.I.; Shapiro, J.; Taksa, I.; Voiskunskii, V.G.: Boolean search : current state and perspectives (1999) 0.07 
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  10. Frants, V.I.; Voiskunskii, V.G.; Shapiro, J.: Automated information retrieval : theory and methods (1997) 0.06 
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  11. Torvik, V.I.; Weeber, M.; Swanson, D.R.; Smalheiser, N.R.: ¬A probabilistic similarity metric for medline mecords : a model for author name disambiguation (2005) 0.06 
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  12. Spitkovsky, V.I.; Chang, A.X.: ¬A cross-lingual dictionary for english Wikipedia concepts (2012) 0.06 
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  13. Swanson, D.R.; Smalheiser, N.R.; Torvik, V.I.: Ranking indirect connections in literature-based discovery : the role of Medical Subject Headings (2006) 0.05 
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  14. Fegley, B.D.; Torvik, V.I.: On the role of poetic versus nonpoetic features in "kindred" and diachronic poetry attribution (2012) 0.05 
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  15. Kleineberg, M.: Context analysis and context indexing : formal pragmatics in knowledge organization (2014) 0.04 
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    Source
    http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&ved=0CDQQFjAE&url=http%3A%2F%2Fdigbib.ubka.uni-karlsruhe.de%2Fvolltexte%2Fdocuments%2F3131107&ei=HzFWVYvGMsiNsgGTyoFI&usg=AFQjCNE2FHUeR9oQTQlNC4TPedv4Mo3DaQ&sig2=Rlzpr7a3BLZZkqZCXXN_IA&bvm=bv.93564037,d.bGg&cad=rja
  16. Spitkovsky, V.; Norvig, P.: From words to concepts and back : dictionaries for linking text, entities and ideas (2012) 0.04 
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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.
  17. Popper, K.R.: Three worlds : the Tanner lecture on human values. Deliverd at the University of Michigan, April 7, 1978 (1978) 0.03 
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    Source
    https%3A%2F%2Ftannerlectures.utah.edu%2F_documents%2Fa-to-z%2Fp%2Fpopper80.pdf&usg=AOvVaw3f4QRTEH-OEBmoYr2J_c7H
  18. Jascó, P.: Searching for images by similarity online (1998) 0.03 
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    Date
    29.11.2004 13:03:22
    Source
    Online. 22(1998) no.6, S.99-102
  19. Hawking, D.; Robertson, S.: On collection size and retrieval effectiveness (2003) 0.03 
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    Date
    14. 8.2005 14:22:22
  20. Buzydlowski, J.W.; White, H.D.; Lin, X.: Term Co-occurrence Analysis as an Interface for Digital Libraries (2002) 0.03 
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    Date
    22. 2.2003 17:25:39
    22. 2.2003 18:16:22

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