Search (9 results, page 1 of 1)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Retrievalalgorithmen"
  1. Elsweiler, D.; Kruschwitz, U.: Interaktives Information Retrieval (2023) 0.00 
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    Pages
    S.403-412
  2. Fuhr, N.: Modelle im Information Retrieval (2023) 0.00 
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    Abstract
    Information-Retrieval-Modelle -(IR-Modelle) spezifizieren, wie zu einer gegebenen Anfrage die Antwortdokumente aus einer Dokumentenkollektion bestimmt werden. Ausgangsbasis jedes Modells sind dabei zunächst bestimmte Annahmen über die Wissensrepräsentation (s. Teil B Methoden und Systeme der Inhaltserschließung) von Fragen und Dokumenten. Hier bezeichnen wir die Elemente dieser Repräsentationen als Terme, wobei es aus der Sicht des Modells egal ist, wie diese Terme aus dem Dokument (und analog aus der von Benutzenden eingegebenen Anfrage) abgeleitet werden: Bei Texten werden hierzu häufig computerlinguistische Methoden eingesetzt, aber auch komplexere automatische oder manuelle Erschließungsverfahren können zur Anwendung kommen. Repräsentationen besitzen ferner eine bestimmte Struktur. Ein Dokument wird meist als Menge oder Multimenge von Termen aufgefasst, wobei im zweiten Fall das Mehrfachvorkommen berücksichtigt wird. Diese Dokumentrepräsentation wird wiederum auf eine sogenannte Dokumentbeschreibung abgebildet, in der die einzelnen Terme gewichtet sein können. Im Folgenden unterscheiden wir nur zwischen ungewichteter (Gewicht eines Terms ist entweder 0 oder 1) und gewichteter Indexierung (das Gewicht ist eine nichtnegative reelle Zahl). Analog dazu gibt es eine Fragerepräsentation; legt man eine natürlichsprachige Anfrage zugrunde, so kann man die o. g. Verfahren für Dokumenttexte anwenden. Alternativ werden auch grafische oder formale Anfragesprachen verwendet, wobei aus Sicht der Modelle insbesondere deren logische Struktur (etwa beim Booleschen Retrieval) relevant ist. Die Fragerepräsentation wird dann in eine Fragebeschreibung überführt.
    Pages
    S.379-398
  3. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.8, S.1010-1025
  4. Reimer, U.: Empfehlungssysteme (2023) 0.00 
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    Pages
    S.485-495
  5. Dang, E.K.F.; Luk, R.W.P.; Allan, J.: ¬A retrieval model family based on the probability ranking principle for ad hoc retrieval (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1140-1154
  6. Liu, J.; Liu, C.: Personalization in text information retrieval : a survey (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.3, S.349-369
  7. Pan, M.; Huang, J.X.; He, T.; Mao, Z.; Ying, Z.; Tu, X.: ¬A simple kernel co-occurrence-based enhancement for pseudo-relevance feedback (2020) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.3, S.264-281
  8. Hammache, A.; Boughanem, M.: Term position-based language model for information retrieval (2021) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.5, S.627-642
  9. Purpura, A.; Silvello, G.; Susto, G.A.: Learning to rank from relevance judgments distributions (2022) 0.00 
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.9, S.1236-1252

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