Search (10 results, page 1 of 1)

  • × language_ss:"e"
  • × theme_ss:"Retrievalalgorithmen"
  • × type_ss:"m"
  1. Dominich, S.: Mathematical foundations of information retrieval (2001) 0.02 
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    Date
    22. 3.2008 12:26:32
    Pages
    XX, 284 S
  2. Grossman, D.A.; Frieder, O.: Information retrieval : algorithms and heuristics (1998) 0.00 
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    Footnote
    Rez. in: JASIS 51(2000) no.11, S.1063-1064 (H.E. Williams)
    Pages
    254 S
  3. Brenner, E.H.: Beyond Boolean : new approaches in information retrieval; the quest for intuitive online search systems past, present & future (1995) 0.00 
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    Pages
    XV,143 S
    Type
    s
  4. Computational information retrieval (2001) 0.00 
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    Pages
    XII,185 S
    Type
    s
  5. Lavrenko, V.: ¬A generative theory of relevance (2009) 0.00 
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    Footnote
    Rez. in: JASIST 60(2009) no.12, S.2587-2588 (R. Luk)
    Pages
    XX, 197 S
  6. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (1999) 0.00 
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    Pages
    XIII, 116 S
  7. Lalmas, M.: XML retrieval (2009) 0.00 
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    Pages
    IX,99 S
  8. Cross-language information retrieval (1998) 0.00 
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    Footnote
    Rez. in: Machine translation review: 1999, no.10, S.26-27 (D. Lewis): "Cross Language Information Retrieval (CLIR) addresses the growing need to access large volumes of data across language boundaries. The typical requirement is for the user to input a free form query, usually a brief description of a topic, into a search or retrieval engine which returns a list, in ranked order, of documents or web pages that are relevant to the topic. The search engine matches the terms in the query to indexed terms, usually keywords previously derived from the target documents. Unlike monolingual information retrieval, CLIR requires query terms in one language to be matched to indexed terms in another. Matching can be done by bilingual dictionary lookup, full machine translation, or by applying statistical methods. A query's success is measured in terms of recall (how many potentially relevant target documents are found) and precision (what proportion of documents found are relevant). Issues in CLIR are how to translate query terms into index terms, how to eliminate alternative translations (e.g. to decide that French 'traitement' in a query means 'treatment' and not 'salary'), and how to rank or weight translation alternatives that are retained (e.g. how to order the French terms 'aventure', 'business', 'affaire', and 'liaison' as relevant translations of English 'affair'). Grefenstette provides a lucid and useful overview of the field and the problems. The volume brings together a number of experiments and projects in CLIR. Mark Davies (New Mexico State University) describes Recuerdo, a Spanish retrieval engine which reduces translation ambiguities by scanning indexes for parallel texts; it also uses either a bilingual dictionary or direct equivalents from a parallel corpus in order to compare results for queries on parallel texts. Lisa Ballesteros and Bruce Croft (University of Massachusetts) use a 'local feedback' technique which automatically enhances a query by adding extra terms to it both before and after translation; such terms can be derived from documents known to be relevant to the query.
    Pages
    VII,182 S
    Type
    s
  9. Berry, M.W.; Browne, M.: Understanding search engines : mathematical modeling and text retrieval (2005) 0.00 
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    Pages
    XVII, 117 S
  10. Langville, A.N.; Meyer, C.D.: Google's PageRank and beyond : the science of search engine rankings (2006) 0.00 
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    Pages
    X, 224 S

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