Abstract
We show that most approaches in probabilistic information retrieval can be regarded as a combination of the three concepts representation, model and abstraction. First, documents and queries have to be represented in a certain form, e.g. as a sets of terms. Probabilistic models use certain assumptions about the distribution of the elements of the representation in relevant and nonrelevant documents in order to estimate the probability of relevance of a document w.r.t. a query. Older approaches based on query-specific relevance feedback are restricted to simple representations and models. Using abstractions from specific documents, terms and queries, more powerful approaches can be realized.
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© 1993 Springer-Verlag Berlin · Heidelberg
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Fuhr, N. (1993). Representations, Models and Abstractions in Probabilistic Information Retrieval. In: Opitz, O., Lausen, B., Klar, R. (eds) Information and Classification. Studies in Classification, Data Analysis and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-50974-2_26
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DOI: https://doi.org/10.1007/978-3-642-50974-2_26
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