Search (158 results, page 1 of 8)

  • × theme_ss:"Computerlinguistik"
  1. Kay, M.; Sparck Jones, K.: Automated language processing (1971) 0.13
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  2. Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1996) 0.11
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  3. Jones, K.: Linguistic searching versus relevance ranking : DR-LINK and TARGET (1999) 0.11
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  4. McCune, B.P.; Tong, R.M.; Dean, J.S.: Rubric: a system for rule-based information retrieval (1985) 0.10
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.440-445.
  5. Porter, M.F.: ¬An algorithm for suffix stripping (1980) 0.10
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.313-316.
  6. Sparck Jones, K.: Synonymy and semantic classification (1986) 0.08
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  7. Tenopir, C.; Cahn, P.: TARGET & FREESTYLE : DIALOG and Mead join the relevance ranks (1994) 0.08
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.446-456.
  8. Zhang, H.J.; Low, C.Y.; Smoliar, S.W.; Lu. J.H.: Video parsing, retrieval and browsing : an integrated and content-based solution (1995) 0.08
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.503-512
  9. Strzalkowski, T.: Robust text processing in automated information retrieval (1994) 0.08
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.317-322.
  10. Hayes, P.J.; Knecht, L.E.; Cellio, M.J.: ¬A news story categorization system (1988) 0.08
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.518-526
  11. Rau, L.F.: Conceptual information extraction and retrieval from natural language input (198) 0.08
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    Footnote
    Wiederabgedruckt in: Readings in information retrieval. Ed.: K. Sparck Jones u. P. Willett. San Francisco: Morgan Kaufmann 1997. S.527-533
  12. Sparck Jones, K.; Kay, M.: Linguistik und Informationswissenschaft (1976) 0.07
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  13. Robertson, S.E.; Sparck Jones, K.: Relevance weighting of search terms (1976) 0.07
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  14. Lewis, D.D.; Sparck Jones, K.: Natural language processing for information retrieval (1997) 0.07
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  15. 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
  16. Grefenstette, G.: Explorations in automatic thesaurus discovery (1994) 0.06
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    Footnote
    Rez. in: Journal of documentation 51(1995) no.4, S.447-448 (K. Sparck Jones)
  17. Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985) 0.06
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    Abstract
    The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.
  18. Sparck Jones, K.; Galliers, J.R.: Evaluating natural language processing systems : an analysis and review (1996) 0.05
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  19. Pritchard-Schoch, T.: Natural language comes of age (1993) 0.02
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    Abstract
    Discusses natural languages and the natural language implementations of Westlaw's full-text legal documents, Westlaw Is Natural. Natural language is not aritificial intelligence but a hybrid of linguistics, mathematics and statistics. Provides 3 classes of retrieval models. Explains how Westlaw processes an English query. Assesses WIN. Covers WIN enhancements; the natural language features of Congressional Quarterly's Washington Alert using a document for a query; the personal librarian front end search software and Dowquest from Dow Jones news/retrieval. Conmsiders whether natural language encourages fuzzy thinking and whether Boolean logic will still be needed
  20. Jones, D.: Analogical natural language processing (1996) 0.02
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Languages

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