Search (104 results, page 5 of 6)

  • × theme_ss:"Retrievalalgorithmen"
  • × year_i:[1990 TO 2000}
  1. Kantor, P.; Kim, M.H.; Ibraev, U.; Atasoy, K.: Estimating the number of relevant documents in enormous collections (1999) 0.00 
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    Abstract
    In assessing information retrieval systems, it is important to know not only the precision of the retrieved set, but also to compare the number of retrieved relevant items to the total number of relevant items. For large collections, such as the TREC test collections, or the World Wide Web, it is not possible to enumerate the entire set of relevant documents. If the retrieved documents are evaluated, a variant of the statistical "capture-recapture" method can be used to estimate the total number of relevant documents, providing the several retrieval systems used are sufficiently independent. We show that the underlying signal detection model supporting such an analysis can be extended in two ways. First, assuming that there are two distinct performance characteristics (corresponding to the chance of retrieving a relevant, and retrieving a given non-relevant document), we show that if there are three or more independent systems available it is possible to estimate the number of relevant documents without actually having to decide whether each individual document is relevant. We report applications of this 3-system method to the TREC data, leading to the conclusion that the independence assumptions are not satisfied. We then extend the model to a multi-system, multi-problem model, and show that it is possible to include statistical dependencies of all orders in the model, and determine the number of relevant documents for each of the problems in the set. Application to the TREC setting will be presented
    Type
    a
  2. Robertson, S.E.; Sparck Jones, K.: Simple, proven approaches to text retrieval (1997) 0.00 
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    Abstract
    This technical note describes straightforward techniques for document indexing and retrieval that have been solidly established through extensive testing and are easy to apply. They are useful for many different types of text material, are viable for very large files, and have the advantage that they do not require special skills or training for searching, but are easy for end users. The document and text retrieval methods described here have a sound theoretical basis, are well established by extensive testing, and the ideas involved are now implemented in some commercial retrieval systems. Testing in the last few years has, in particular, shown that the methods presented here work very well with full texts, not only title and abstracts, and with large files of texts containing three quarters of a million documents. These tests, the TREC Tests (see Harman 1993 - 1997; IP&M 1995), have been rigorous comparative evaluations involving many different approaches to information retrieval. These techniques depend an the use of simple terms for indexing both request and document texts; an term weighting exploiting statistical information about term occurrences; an scoring for request-document matching, using these weights, to obtain a ranked search output; and an relevance feedback to modify request weights or term sets in iterative searching. The normal implementation is via an inverted file organisation using a term list with linked document identifiers, plus counting data, and pointers to the actual texts. The user's request can be a word list, phrases, sentences or extended text.
  3. Evans, R.: Beyond Boolean : relevance ranking, natural language and the new search paradigm (1994) 0.00 
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    Abstract
    New full-text search engines that employ relevance ranking have become available online services. These software tools provide increased ease of use by making natural language queries possible, and deliver superior recall. Even inexperienced end users can execute searchers with good results. For experienced database searchers, the ranked search engines offer a technology that is complementary to structured Boolean strategy, not necessarily a replacement. Even traditional Boolean queries become useful when the results are ranked by probable relevance, such ranking can free users from overwhelming output. Relevance ranking also permits the use of statistical inference methods to find related terms. using such tools to their best advantage requires rethinking some basic techniques, such as progressively narrowing queries until the retrieved set is small enough. users should broaden their search to maximize recall, then browse retrieved documents or pare the set down from the top
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  4. Quint, B.: Check out the new RANK command on DIALOG (1993) 0.00 
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  5. Willett, P.: Best-match text retrieval (1993) 0.00 
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  6. Robertson, S.E.: OKAPI at TREC-1 (1994) 0.00 
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    Abstract
    Describes the work carried out on the TREC-2 project following the results of the TREC-1 project. Experiments were conducted on the OKAPI experimental text information retrieval system which investigated a number of alternative probabilistic term weighting functions in place of the 'standard' Robertson Sparck Jones weighting functions used in TREC-1
  7. Courtois, M.P.; Berry, M.W.: Results ranking in Web search engines (1999) 0.00 
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  8. Koopman, R.: ¬Ein OPAC mit Gewichtungsalgorithmen : Der PICA Micro OPC (1996) 0.00 
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  9. Zhang, W.; Korf, R.E.: Performance of linear-space search algorithms (1995) 0.00 
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  10. Baeza-Yates, R.A.: Introduction to data structures and algorithms related to information retrieval (1992) 0.00 
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  11. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.00 
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  12. Rajashekar, T.B.; Croft, W.B.: Combining automatic and manual index representations in probabilistic retrieval (1995) 0.00 
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    Abstract
    Results from research in information retrieval have suggested that significant improvements in retrieval effectiveness can be obtained by combining results from multiple index representioms, query formulations, and search strategies. The inference net model of retrieval, which was designed from this point of view, treats information retrieval as an evidental reasoning process where multiple sources of evidence about document and query content are combined to estimate relevance probabilities. Uses a system based on this model to study the retrieval effectiveness benefits of combining these types of document and query information that are found in typical commercial databases and information services. The results indicate that substantial real benefits are possible
    Type
    a
  13. Robertson, S.E.: OKAPI at TREC-3 (1995) 0.00 
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    Abstract
    Reports text information retrieval experiments performed as part of the 3 rd round of Text Retrieval Conferences (TREC) using the Okapi online catalogue system at City University, UK. The emphasis in TREC-3 was: further refinement of term weighting functions; an investigation of run time passage determination and searching; expansion of ad hoc queries by terms extracted from the top documents retrieved by a trial search; new methods for choosing query expansion terms after relevance feedback, now split into methods of ranking terms prior to selection and subsequent selection procedures; and the development of a user interface procedure within the new TREC interactive search framework
  14. Abu-Salem, H.; Al-Omari, M.; Evens, M.W.: Stemming methodologies over individual query words for an Arabic information retrieval system (1999) 0.00 
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    Abstract
    Stemming is one of the most important factors that affect the performance of information retrieval systems. This article investigates how to improve the performance of an Arabic information retrieval system by imposing the retrieval method over individual words of a query depending on the importance of the WORD, the STEM, or the ROOT of the query terms in the database. This method, called Mxed Stemming, computes term importance using a weighting scheme that use the Term Frequency (TF) and the Inverse Document Frequency (IDF), called TFxIDF. An extended version of the Arabic IRS system is designed, implemented, and evaluated to reduce the number of irrelevant documents retrieved. The results of the experiment suggest that the proposed method outperforms the Word index method using the TFxIDF weighting scheme. It also outperforms the Stem index method using the Binary weighting scheme but does not outperform the Stem index method using the TFxIDF weighting scheme, and again it outperforms the Root index method using the Binary weighting scheme but does not outperform the Root index method using the TFxIDF weighting scheme
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  15. Wilhelmy, A.: Phonetische Ähnlichkeitssuche in Datenbanken (1991) 0.00 
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  16. Fuhr, N.: Zur Überwindung der Diskrepanz zwischen Retrievalforschung und -praxis (1990) 0.00 
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  17. Can, F.: Incremental clustering for dynamic information processing (1993) 0.00 
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  18. Chang, R.: ¬The development of indexing technology (1993) 0.00 
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  19. Ojala, M.: Decisions, decisions, RANK decisions (1994) 0.00 
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  20. Robertson, A.M.; Willett, P.: Use of genetic algorithms in information retrieval (1995) 0.00 
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    Abstract
    Reviews the basic techniques involving genetic algorithms and their application to 2 problems in information retrieval: the generation of equifrequent groups of index terms; and the identification of optimal query and term weights. The algorithm developed for the generation of equifrequent groupings proved to be effective in operation, achieving results comparable with those obtained using a good deterministic algorithm. The algorithm developed for the identification of optimal query and term weighting involves fitness function that is based on full relevance information

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