Search (88 results, page 1 of 5)

  • × theme_ss:"Retrievalalgorithmen"
  1. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
    Source
    Deutscher Dokumentartag 1985, Nürnberg, 1.-4.10.1985: Fachinformation: Methodik - Management - Markt; neue Entwicklungen, Berufe, Produkte. Bearb.: H. Strohl-Goebel
  2. Information retrieval : data structures and algorithms (1992) 0.02 
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    Content
    An edited volume containing data structures and algorithms for information retrieval including a disk with examples written in C. for prgrammers and students interested in parsing text, automated indexing, its the first collection in book form of the basic data structures and algorithms that are critical to the storage and retrieval of documents. ------------------Enthält die Kapitel: FRAKES, W.B.: Introduction to information storage and retrieval systems; BAEZA-YATES, R.S.: Introduction to data structures and algorithms related to information retrieval; HARMAN, D. u.a.: Inverted files; FALOUTSOS, C.: Signature files; GONNET, G.H. u.a.: New indices for text: PAT trees and PAT arrays; FORD, D.A. u. S. CHRISTODOULAKIS: File organizations for optical disks; FOX, C.: Lexical analysis and stoplists; FRAKES, W.B.: Stemming algorithms; SRINIVASAN, P.: Thesaurus construction; BAEZA-YATES, R.A.: String searching algorithms; HARMAN, D.: Relevance feedback and other query modification techniques; WARTIK, S.: Boolean operators; WARTIK, S. u.a.: Hashing algorithms; HARMAN, D.: Ranking algorithms; FOX, E.: u.a.: Extended Boolean models; RASMUSSEN, E.: Clustering algorithms; HOLLAAR, L.: Special-purpose hardware for information retrieval; STANFILL, C.: Parallel information retrieval algorithms
  3. Liu, A.; Zou, Q.; Chu, W.W.: Configurable indexing and ranking for XML information retrieval (2004) 0.02 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  4. Yu, K.; Tresp, V.; Yu, S.: ¬A nonparametric hierarchical Bayesian framework for information filtering (2004) 0.02 
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    Source
    SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a
  5. Pfeifer, U.; Pennekamp, S.: Incremental processing of vague queries in interactive retrieval systems (1997) 0.02 
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    Source
    Hypertext - Information Retrieval - Multimedia '97: Theorien, Modelle und Implementierungen integrierter elektronischer Informationssysteme. Proceedings HIM '97. Hrsg.: N. Fuhr u.a
  6. Chang, C.-H.; Hsu, C.-C.: Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval (1998) 0.01 
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    Date
    1. 8.1996 22:08:06
  7. Kanaeva, Z.: Ranking: Google und CiteSeer (2005) 0.01 
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    Date
    20. 3.2005 16:23:22
    Source
    Information - Wissenschaft und Praxis. 56(2005) H.2, S.87-92
  8. Fan, W.; Fox, E.A.; Pathak, P.; Wu, H.: ¬The effects of fitness functions an genetic programming-based ranking discovery for Web search (2004) 0.01 
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    Date
    31. 5.2004 19:22:06
  9. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.01 
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    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  10. Habernal, I.; Konopík, M.; Rohlík, O.: Question answering (2012) 0.01 
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    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  11. Mandl, T.: Tolerantes Information Retrieval : Neuronale Netze zur Erhöhung der Adaptivität und Flexibilität bei der Informationssuche (2001) 0.01 
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    Footnote
    Rez. in: nfd - Information 54(2003) H.6, S.379-380 (U. Thiel): "Kannte G. Salton bei der Entwicklung des Vektorraummodells die kybernetisch orientierten Versuche mit assoziativen Speicherstrukturen? An diese und ähnliche Vermutungen, die ich vor einigen Jahren mit Reginald Ferber und anderen Kollegen diskutierte, erinnerte mich die Thematik des vorliegenden Buches. Immerhin lässt sich feststellen, dass die Vektorrepräsentation eine genial einfache Darstellung sowohl der im Information Retrieval (IR) als grundlegende Datenstruktur benutzten "inverted files" als auch der assoziativen Speichermatrizen darstellt, die sich im Laufe der Zeit Über Perzeptrons zu Neuronalen Netzen (NN) weiterentwickelten. Dieser formale Zusammenhang stimulierte in der Folge eine Reihe von Ansätzen, die Netzwerke im Retrieval zu verwenden, wobei sich, wie auch im vorliegenden Band, hybride Ansätze, die Methoden aus beiden Disziplinen kombinieren, als sehr geeignet erweisen. Aber der Reihe nach... Das Buch wurde vom Autor als Dissertation beim Fachbereich IV "Sprachen und Technik" der Universität Hildesheim eingereicht und resultiert aus einer Folge von Forschungsbeiträgen zu mehreren Projekten, an denen der Autor in der Zeit von 1995 bis 2000 an verschiedenen Standorten beteiligt war. Dies erklärt die ungewohnte Breite der Anwendungen, Szenarien und Domänen, in denen die Ergebnisse gewonnen wurden. So wird das in der Arbeit entwickelte COSIMIR Modell (COgnitive SIMilarity learning in Information Retrieval) nicht nur anhand der klassischen Cranfield-Kollektion evaluiert, sondern auch im WING-Projekt der Universität Regensburg im Faktenretrieval aus einer Werkstoffdatenbank eingesetzt. Weitere Versuche mit der als "Transformations-Netzwerk" bezeichneten Komponente, deren Aufgabe die Abbildung von Gewichtungsfunktionen zwischen zwei Termräumen ist, runden das Spektrum der Experimente ab. Aber nicht nur die vorgestellten Resultate sind vielfältig, auch der dem Leser angebotene "State-of-the-Art"-Überblick fasst in hoch informativer Breite Wesentliches aus den Gebieten IR und NN zusammen und beleuchtet die Schnittpunkte der beiden Bereiche. So werden neben den Grundlagen des Text- und Faktenretrieval die Ansätze zur Verbesserung der Adaptivität und zur Beherrschung von Heterogenität vorgestellt, während als Grundlagen Neuronaler Netze neben einer allgemeinen Einführung in die Grundbegriffe u.a. das Backpropagation-Modell, KohonenNetze und die Adaptive Resonance Theory (ART) geschildert werden. Einweiteres Kapitel stellt die bisherigen NN-orientierten Ansätze im IR vor und rundet den Abriss der relevanten Forschungslandschaft ab. Als Vorbereitung der Präsentation des COSIMIR-Modells schiebt der Autor an dieser Stelle ein diskursives Kapitel zum Thema Heterogenität im IR ein, wodurch die Ziele und Grundannahmen der Arbeit noch einmal reflektiert werden. Als Dimensionen der Heterogenität werden der Objekttyp, die Qualität der Objekte und ihrer Erschließung und die Mehrsprachigkeit genannt. Wenn auch diese Systematik im Wesentlichen die Akzente auf Probleme aus den hier tangierten Projekten legt, und weniger eine umfassende Aufbereitung z.B. der Literatur zum Problem der Relevanz anstrebt, ist sie dennoch hilfreich zum Verständnis der in den nachfolgenden Kapitel oft nur implizit angesprochenen Designentscheidungen bei der Konzeption der entwickelten Prototypen. Der Ansatz, Heterogenität durch Transformationen zu behandeln, wird im speziellen Kontext der NN konkretisiert, wobei andere Möglichkeiten, die z.B. Instrumente der Logik und Probabilistik einzusetzen, nur kurz diskutiert werden. Eine weitergehende Analyse hätte wohl auch den Rahmen der Arbeit zu weit gespannt,
  12. Cross-language information retrieval (1998) 0.01 
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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
  13. Wu, H.; Salton, G.: ¬The estimation of term relevance weights using relevance feedback (1981) 0.01 
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  14. Hüther, H.: Selix im DFG-Projekt Kascade (1998) 0.01 
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    Knowledge Management und Kommunikationssysteme: Proceedings des 6. Internationalen Symposiums für Informationswissenschaft (ISI '98) Prag, 3.-7. November 1998 / Hochschulverband für Informationswissenschaft (HI) e.V. Konstanz ; Fachrichtung Informationswissenschaft der Universität des Saarlandes, Saarbrücken. Hrsg.: Harald H. Zimmermann u. Volker Schramm
  15. Dreßler, H.: Fuzzy Information Retrieval (2008) 0.01 
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    Information - Wissenschaft und Praxis. 59(2008) H.6/7, S.351-352
  16. Jacso, P.: Testing the calculation of a realistic h-index in Google Scholar, Scopus, and Web of Science for F. W. Lancaster (2008) 0.01 
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    Abstract
    This paper focuses on the practical limitations in the content and software of the databases that are used to calculate the h-index for assessing the publishing productivity and impact of researchers. To celebrate F. W. Lancaster's biological age of seventy-five, and "scientific age" of forty-five, this paper discusses the related features of Google Scholar, Scopus, and Web of Science (WoS), and demonstrates in the latter how a much more realistic and fair h-index can be computed for F. W. Lancaster than the one produced automatically. Browsing and searching the cited reference index of the 1945-2007 edition of WoS, which in my estimate has over a hundred million "orphan references" that have no counterpart master records to be attached to, and "stray references" that cite papers which do have master records but cannot be identified by the matching algorithm because of errors of omission and commission in the references of the citing works, can bring up hundreds of additional cited references given to works of an accomplished author but are ignored in the automatic process of calculating the h-index. The partially manual process doubled the h-index value for F. W. Lancaster from 13 to 26, which is a much more realistic value for an information scientist and professor of his stature.
    Object
    h-index
  17. White, H. D.: Co-cited author retrieval and relevance theory : examples from the humanities (2015) 0.01 
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  18. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.01 
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  19. Loughran, H.: ¬A review of nearest neighbour information retrieval (1994) 0.01 
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  20. Weinstein, A.: Hochprozentig : Tipps and tricks für ein Top-Ranking (2002) 0.01 
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