Search (31 results, page 1 of 2)

  • × language_ss:"e"
  • × theme_ss:"Retrievalstudien"
  • × year_i:[1990 TO 2000}
  1. Cross-language information retrieval (1998) 0.08
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    Content
    Enthält die Beiträge: GREFENSTETTE, G.: The Problem of Cross-Language Information Retrieval; DAVIS, M.W.: On the Effective Use of Large Parallel Corpora in Cross-Language Text Retrieval; BALLESTEROS, L. u. W.B. CROFT: Statistical Methods for Cross-Language Information Retrieval; Distributed Cross-Lingual Information Retrieval; Automatic Cross-Language Information Retrieval Using Latent Semantic Indexing; EVANS, D.A. u.a.: Mapping Vocabularies Using Latent Semantics; PICCHI, E. u. C. PETERS: Cross-Language Information Retrieval: A System for Comparable Corpus Querying; YAMABANA, K. u.a.: A Language Conversion Front-End for Cross-Language Information Retrieval; GACHOT, D.A. u.a.: The Systran NLP Browser: An Application of Machine Translation Technology in Cross-Language Information Retrieval; HULL, D.: A Weighted Boolean Model for Cross-Language Text Retrieval; SHERIDAN, P. u.a. Building a Large Multilingual Test Collection from Comparable News Documents; OARD; D.W. u. B.J. DORR: Evaluating Cross-Language Text Filtering Effectiveness
    Footnote
    Christian Fluhr at al (DIST/SMTI, France) outline the EMIR (European Multilingual Information Retrieval) and ESPRIT projects. They found that using SYSTRAN to machine translate queries and to access material from various multilingual databases produced less relevant results than a method referred to as 'multilingual reformulation' (the mechanics of which are only hinted at). An interesting technique is Latent Semantic Indexing (LSI), described by Michael Littman et al (Brown University) and, most clearly, by David Evans et al (Carnegie Mellon University). LSI involves creating matrices of documents and the terms they contain and 'fitting' related documents into a reduced matrix space. This effectively allows queries to be mapped onto a common semantic representation of the documents. Eugenio Picchi and Carol Peters (Pisa) report on a procedure to create links between translation equivalents in an Italian-English parallel corpus. The links are used to construct parallel linguistic contexts in real-time for any term or combination of terms that is being searched for in either language. Their interest is primarily lexicographic but they plan to apply the same procedure to comparable corpora, i.e. to texts which are not translations of each other but which share the same domain. Kiyoshi Yamabana et al (NEC, Japan) address the issue of how to disambiguate between alternative translations of query terms. Their DMAX (double maximise) method looks at co-occurrence frequencies between both source language words and target language words in order to arrive at the most probable translation. The statistical data for the decision are derived, not from the translation texts but independently from monolingual corpora in each language. An interactive user interface allows the user to influence the selection of terms during the matching process. Denis Gachot et al (SYSTRAN) describe the SYSTRAN NLP browser, a prototype tool which collects parsing information derived from a text or corpus previously translated with SYSTRAN. The user enters queries into the browser in either a structured or free form and receives grammatical and lexical information about the source text and/or its translation.
    The retrieved output from a query including the phrase 'big rockets' may be, for instance, a sentence containing 'giant rocket' which is semantically ranked above 'military ocket'. David Hull (Xerox Research Centre, Grenoble) describes an implementation of a weighted Boolean model for Spanish-English CLIR. Users construct Boolean-type queries, weighting each term in the query, which is then translated by an on-line dictionary before being applied to the database. Comparisons with the performance of unweighted free-form queries ('vector space' models) proved encouraging. Two contributions consider the evaluation of CLIR systems. In order to by-pass the time-consuming and expensive process of assembling a standard collection of documents and of user queries against which the performance of an CLIR system is manually assessed, Páriac Sheridan et al (ETH Zurich) propose a method based on retrieving 'seed documents'. This involves identifying a unique document in a database (the 'seed document') and, for a number of queries, measuring how fast it is retrieved. The authors have also assembled a large database of multilingual news documents for testing purposes. By storing the (fairly short) documents in a structured form tagged with descriptor codes (e.g. for topic, country and area), the test suite is easily expanded while remaining consistent for the purposes of testing. Douglas Ouard and Bonne Dorr (University of Maryland) describe an evaluation methodology which appears to apply LSI techniques in order to filter and rank incoming documents designed for testing CLIR systems. The volume provides the reader an excellent overview of several projects in CLIR. It is well supported with references and is intended as a secondary text for researchers and practitioners. It highlights the need for a good, general tutorial introduction to the field."
  2. Belkin, N.J.: ¬An overview of results from Rutgers' investigations of interactive information retrieval (1998) 0.07
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    Date
    22. 9.1997 19:16:05
    Source
    Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
  3. Gilchrist, A.: Research and consultancy (1998) 0.06
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    Source
    Library and information work worldwide 1998. Ed.: M.B. Line et al
  4. Boros, E.; Kantor, P.B.; Neu, D.J.: Pheromonic representation of user quests by digital structures (1999) 0.06
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    Abstract
    In a novel approach to information finding in networked environments, each user's specific purpose or "quest" can be represented in numerous ways. The most familiar is a list of keywords, or a natural language sentence or paragraph. More effective is an extended text that has been judged as to relevance. This forms the basis of relevance feedback, as it is used in information retrieval. In the "Ant World" project (Ant World, 1999; Kantor et al., 1999b; Kantor et al., 1999a), the items to be retrieved are not documents, but rather quests, represented by entire collections of judged documents. In order to save space and time we have developed methods for representing these complex entities in a short string of about 1,000 bytes, which we call a "Digital Information Pheromone" (DIP). The principles for determining the DIP for a given quest, and for matching DIPs to each other are presented. The effectiveness of this scheme is explored with some applications to the large judged collections of TREC documents
  5. Ng, K.B.; Loewenstern, D.; Basu, C.; Hirsh, H.; Kantor, P.B.: Data fusion of machine-learning methods for the TREC5 routing tak (and other work) (1997) 0.04
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    Date
    27. 2.1999 20:59:22
  6. Ellis, D.: Progress and problems in information retrieval (1996) 0.03
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    Date
    26. 7.2002 20:22:46
    Footnote
    Rez. in: Managing information 3(1996) no.10, S.49 (D. Bawden); Program 32(1998) no.2, S.190-192 (C. Revie)
  7. Prabha, C.: ¬The large retrieval phenomenon (1991) 0.02
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  8. Buckley, C.; Singhal, A.; Mitra, M.; Salton, G.: New retrieval approaches using SMART : TREC 4 (1996) 0.02
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  9. Burnett, M.; Fisher, C.; Jones, R.: InTEXT precision indexing in TREC4 (1996) 0.02
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  10. Singhal, A.; Buckley, C.; Mitra, M.: Using query zoning and correlation with SMART : TREC 5 (1997) 0.02
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  11. Lai, K.F.; Ngo, C.-W.: Experiments on routing, filtering and Chinese text retrieval in TREC-5 (1997) 0.02
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  12. Lundquist, C.; Grossmann, D.A.; Reichart, J.; Holmes, D.O.; Chowdhury, A.; Frieder, O.: Using relevance feedback within the relational model for TREC-5 (1997) 0.01
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  13. Belkin, N.J.; Cabezas, A.; Cool, C.; Kim, K.; Ng, K.B.; Park, S.; Pressman, R.; Rieh, S.; Savage, P.; Xie, H.: Rutgers interactive track at TREC-5 (1997) 0.01
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  14. Tomaiuolo, N.G.; Parker, J.: Maximizing relevant retrieval : keyword and natural language searching (1998) 0.01
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    Source
    Online. 22(1998) no.6, S.57-58
  15. Dalrymple, P.W.: Retrieval by reformulation in two library catalogs : toward a cognitive model of searching behavior (1990) 0.01
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    Date
    22. 7.2006 18:43:54
  16. Salampasis, M.; Tait, J.; Bloor, C.: Evaluation of information-seeking performance in hypermedia digital libraries (1998) 0.01
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  17. Wood, F.; Ford, N.; Walsh, C.: ¬The effect of postings information on search behaviour (1994) 0.01
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  18. Khan, K.; Locatis, C.: Searching through cyberspace : the effects of link display and link density on information retrieval from hypertext on the World Wide Web (1998) 0.01
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  19. ¬The Second Text Retrieval Conference : TREC-2 (1995) 0.01
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    Content
    Enthält die Beiträge: HARMAN, D.: Overview of the Second Text Retrieval Conference (TREC-2); SPRACK JONES, K.: Reflections on TREC; BUCKLEY, C., J. ALLAN u. G. SALTON: Automatic routing and retrieval using SMART: TREC-2; CALLAN, J.P., W.B. CROFT u. J. BROGLIO: TREC and TIPSTER experiments with INQUERY; ROBERTSON, S.R., S. WALKER u. M.M. HANCOCK-BEAULIEU: Large test collection experiments on an operational, interactive system: OKAPI at TREC; ZOBEL, J., A. MOFFAT, R. WILKINSON u. R. SACKS-DAVIS: Efficient retrieval of partial documents; METTLER, M. u. F. NORDBY: TREC routing experiments with the TRW/Paracel Fast Data Finder; EVANS, D.A. u. R.G. LEFFERTS: CLARIT-TREC experiments; STRZALKOWSKI, T.: Natural language information retrieval; CAID, W.R., S.T. DUMAIS u. S.I. GALLANT: Learned vector-space models for document retrieval; BELKIN, N.J. P. KANTOR, E.A. FOX u. J.A. SHAW: Combining the evidence of multiple query representations for information retrieval
  20. Buckley, C.; Allan, J.; Salton, G.: Automatic routing and retrieval using Smart : TREC-2 (1995) 0.01
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