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  2. Relevance feedback - Wikipedia

    en.wikipedia.org/wiki/Relevance_feedback

    Relevance feedback is a feature of some information retrieval systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types ...

  3. Query expansion - Wikipedia

    en.wikipedia.org/wiki/Query_expansion

    This is the so called pseudo-relevance feedback (PRF). [6] Pseudo-relevance feedback is efficient in average but can damage results for some queries, [7] especially difficult ones since the top retrieved documents are probably non-relevant. Pseudo-relevant documents are used to find expansion candidate terms that co-occur with many query terms. [8]

  4. Extended Boolean model - Wikipedia

    en.wikipedia.org/wiki/Extended_Boolean_model

    This way a document may be somewhat relevant if it matches some of the queried terms and will be returned as a result, whereas in the Standard Boolean model it wasn't. [ 1 ] Thus, the extended Boolean model can be considered as a generalization of both the Boolean and vector space models; those two are special cases if suitable settings and ...

  5. Rocchio algorithm - Wikipedia

    en.wikipedia.org/wiki/Rocchio_algorithm

    The Rocchio algorithm is based on a method of relevance feedback found in information retrieval systems which stemmed from the SMART Information Retrieval System developed between 1960 and 1964. Like many other retrieval systems, the Rocchio algorithm was developed using the vector space model .

  6. Concept search - Wikipedia

    en.wikipedia.org/wiki/Concept_search

    Relevance feedback is a feature that helps users determine if the results returned for their queries meet their information needs. In other words, relevance is assessed relative to an information need, not a query. A document is relevant if it addresses the stated information need, not because it just happens to contain all the words in the ...

  7. Information retrieval - Wikipedia

    en.wikipedia.org/wiki/Information_retrieval

    1979: C. J. van Rijsbergen published Information Retrieval (Butterworths). Heavy emphasis on probabilistic models. 1979: Tamas Doszkocs implemented the CITE natural language user interface for MEDLINE at the National Library of Medicine. The CITE system supported free form query input, ranked output and relevance feedback. [15] 1980s

  8. C, The Complete Reference - Wikipedia

    en.wikipedia.org/wiki/C,_The_Complete_Reference

    C, The Complete Reference [1] is a book on computer programming written by Herbert Schildt. The book gives an in-depth coverage of the C language and function libraries features. [2] [3] The first edition was released by Osbourne in 1987. The current version is 4th. Last revision: January 13th, 2018. [4]

  9. Query language - Wikipedia

    en.wikipedia.org/wiki/Query_language

    Broadly, query languages can be classified according to whether they are database query languages or information retrieval query languages. The difference is that a database query language attempts to give factual answers to factual questions, while an information retrieval query language attempts to find documents containing information that is relevant to an area of inquiry.